Patient-specific glucose prediction system and method

Abstract

The present invention provides an infusion device and related medical devices, a patient data management system and a method for monitoring a patient's physiological condition. An exemplary method for monitoring a patient's physiological condition involves obtaining current measurement data of the patient's physiological condition provided by a sensing device; obtaining user input indicating one or more future events associated with the patient; and in response to the user input, using one or more prediction models associated with the patient, determining a prediction of the patient's future physiological condition based at least in part on the current measurement data and the one or more future events; and displaying a graphical representation of the prediction on a display device.

Description

[0001] This application is a divisional application of the PCT international application (PCT application number: PCT / US2018 / 024099) that has entered the national phase and has application number: 201880032844.0, application date: March 23, 2018, and invention name: “Patient-specific glucose prediction system and method”.

[0002] priority

[0003] This PCT application claims the benefit of and priority to the following patent applications: U.S. Patent Application Serial No. 15 / 933,264, filed March 22, 2018; U.S. Patent Application Serial No. 15 / 933,258, filed March 22, 2018; U.S. Patent Application Serial No. 15 / 933,266, filed March 22, 2018; U.S. Patent Application Serial No. 15 / 933,268, filed March 22, 2018; U.S. Patent Application Serial No. 15 / 933,272, filed March 22, 2018; U.S. Patent Application Serial No. 15 / 933,275, filed March 22, 2018; U.S. Patent Application Serial No. 15 / 933,277, filed March 22, 2018; U.S. Patent Application Serial No. 15 / 933,278, filed July 18, 2017 No. 62 / 476,493 filed on March 24, 2017; No. 62 / 476,506 filed on March 24, 2017; and No. 62 / 476,517 filed on March 24, 2017. Technical Field

[0004] Embodiments of the subject matter described herein relate generally to medical devices and related patient monitoring systems, and more particularly, embodiments of the subject matter relate to database systems that facilitate improved patient-specific queries, predictions, and recommendations. Background Art

[0005] Infusion pump devices and systems are well known in the medical field for delivering or dispensing agents such as insulin or another prescription drug to a patient. The use of infusion pump therapy has been increasing, particularly for insulin delivery to diabetic patients. Continuous insulin infusion provides better control of diabetic symptoms, and therefore control schemes have been developed that allow insulin infusion pumps to monitor and regulate a patient's blood glucose level in a substantially continuous and autonomous manner, for example, throughout the night while the patient is sleeping.

[0006] Regulating blood glucose levels is complicated by the variation in response time of the type of insulin used and the individual insulin response of each patient. In addition, the patient's daily activities and experiences may cause the patient's insulin response to change during the day or from one day to the next. Therefore, there is a need to help improve glucose control, which takes into account many different variables in a personalized manner. In addition, the effects and efficacy of different treatment regimens may vary from patient to patient. Therefore, it is also desirable to better understand how the condition of an individual patient can be affected by various behaviors, or how different therapies or behaviors can improve the regulation of the patient's condition. In conjunction with the accompanying drawings and the previous background technology, other desired features and characteristics of the methods, devices and systems described herein will become apparent according to the subsequent specific embodiments and the appended claims. Summary of the invention

[0007] The present invention provides an infusion device and related medical devices, a patient data management system and a method for monitoring a patient's physiological condition. An embodiment of the method for monitoring a patient's physiological condition includes: obtaining current measurement data of the patient's physiological condition provided by a sensing device at a computing device; obtaining user input indicating one or more future events associated with the patient at the computing device; and in response to the user input, using one or more prediction models associated with the patient, determining a prediction of the patient's future physiological condition based at least in part on the current measurement data and the one or more future events; and displaying a graphical representation of the prediction on a display device at the computing device.

[0008] In another embodiment, an apparatus of an electronic device is provided. The electronic device includes: a communication interface for receiving current measurement data of a patient's physiological condition from a sensing device; a display device having a graphical user interface display presented thereon; a user interface for obtaining user input indicating one or more future events; and a control system coupled to the communication interface, the display device, and the user interface to determine a prediction of a future physiological condition of the patient based at least in part on the current measurement data and the one or more future events using one or more prediction models associated with the patient, and displaying a graphical representation of the prediction within the graphical user interface display of the display device.

[0009] In another embodiment, a method for monitoring a patient's glucose level is provided. The method includes: obtaining, at a computing device, current sensor glucose measurement data of the patient from a glucose sensing device; obtaining, at the computing device, user input indicating one or more future events of the patient via a user interface; determining, at the computing device, a future simulated glucose level of the patient based at least in part on the current sensor glucose measurement data and the one or more future events using a plurality of different prediction models associated with the patient, wherein the plurality of different prediction models include an hourly forecast model associated with the patient; and displaying, on a display device associated with the computing device, a graphical representation of the simulated glucose level relative to future time.

[0010] In another embodiment, a method for monitoring a patient's physiological condition includes: obtaining current measurement data of the patient's physiological condition from a sensing device; predicting one or more events that may affect the patient's physiological condition at one or more different times in the future based at least in part on historical event data associated with the patient; using a predictive model associated with the patient, determining a plurality of predicted values ​​of the patient's physiological condition associated with a plurality of different time periods in the future based at least in part on the current measurement data and the one or more events; and displaying the plurality of predicted values ​​relative to a plurality of different time periods in the future on a display device.

[0011] In another embodiment, a system is provided, the system comprising: a display device; a sensing device for obtaining current measurement data of a patient's physiological condition; and a control system coupled to the display device and the sensing device so as to determine a plurality of forecast hourly averages of the patient's future physiological condition based at least in part on the current measurement data using an hourly forecast model associated with the patient, and to display the plurality of forecast hourly averages on the display device.

[0012] In another embodiment, a method for monitoring a patient's glucose level is provided. The method includes: determining an hourly forecast model for the patient based at least in part on a relationship between historical glucose measurement data of the patient and historical event data associated with the patient; obtaining current glucose measurement data of the patient from a glucose sensing device; predicting one or more events that may affect the patient's glucose level at one or more different times in the future based at least in part on the historical event data associated with the patient; using the hourly forecast model associated with the patient, determining multiple future hourly forecast average glucose values ​​for the patient based at least in part on the current glucose measurement data and the one or more events; and displaying a graphical representation of the multiple future hourly forecast average glucose values ​​on a display device.

[0013] In another embodiment, a method for monitoring a patient's physiological condition includes: obtaining current measurement data for the patient's physiological condition from a sensing device; determining a first plurality of prediction values ​​for the patient's physiological condition in the future based at least in part on the current measurement data using a first prediction model; determining a second plurality of prediction values ​​for the patient's physiological condition in the future based at least in part on the current measurement data using a second prediction model different from the first prediction model; determining a collective prediction for the patient's physiological condition relative to a future time based at least in part on the first plurality of prediction values, the second plurality of prediction values, and weighting factors associated with the respective first prediction models and second prediction models, wherein the weighting factors vary relative to the future time based on a relationship between a first reliability metric associated with the first prediction model and a second reliability metric associated with the second prediction model; and displaying a graphical indication of the collective prediction for the patient's physiological condition relative to the future time on a display device.

[0014] Another method for monitoring a patient's physiological condition includes: obtaining current measurement data for the patient's physiological condition from a sensing device; determining a plurality of prediction values ​​indicating the physiological condition for a future time based at least in part on the current measurement data using a plurality of different prediction models associated with the patient, wherein each of the plurality of prediction values ​​is associated with a corresponding prediction model in the plurality of different prediction models, determining a reliability metric associated with the corresponding prediction model for each corresponding prediction model in the plurality of different prediction models based at least in part on a relationship between the future time and the current time of the day, determining a weighting factor associated with the corresponding prediction model for each corresponding prediction model in the plurality of different prediction models based at least in part on the reliability metric associated with the corresponding prediction model, determining a collective prediction value for the patient's physiological condition as a weighted average of the corresponding prediction values ​​of the plurality of prediction values ​​and the weighting factors associated with the corresponding prediction models, and displaying a graphical indication of the collective prediction value of the patient's physiological condition associated with the future time.

[0015] In another embodiment, an apparatus of an electronic device is provided. The electronic device includes: a communication interface for receiving current measurement data of a patient's physiological condition from a sensing device; a display device having a graphical user interface display including a graphical representation of the current measurement data; a user interface for obtaining user input for adjusting the graphical user interface display to view the future; and a control system coupled to the communication interface, the display device, and the user interface to determine a first plurality of predicted values ​​of the patient's future physiological condition based at least in part on the current measurement data using a first prediction model; determine a second plurality of predicted values ​​of the patient's future physiological condition based at least in part on the current measurement data using a second prediction model different from the first prediction model; determine a collective prediction of the patient's physiological condition relative to a future time based at least in part on the first plurality of predicted values ​​and the second plurality of predicted values; and display a graphical representation of the collective prediction on the graphical user interface display in response to the user input.

[0016] In another embodiment, a database system is provided. The database system includes: a database for maintaining data related to a plurality of entities; and a computing device coupled to the database to identify relationships between different pairs of the plurality of entities, generate metadata defining a graph structure, the graph structure maintaining relationships between different entities of the plurality of entities at a plurality of different logical levels, and store the metadata in the database.

[0017] In another embodiment, a method for managing a database maintaining data related to a plurality of patients is provided. The method includes: analyzing, by a computing device, a graph data structure of a logical layer in the database, the graph data structure being defined by metadata in the database and the graph data structure comprising a plurality of entities, wherein each entity in the plurality of entities maintains a logical relationship with one or more fields of observation data associated with a corresponding patient in a plurality of patients; identifying, by the computing device, a relationship between a pair of entities in the plurality of entities in the logical layer; and updating, by the computing device, the metadata in the database to create a link between the pair of entities.

[0018] In another embodiment, a system includes: a plurality of medical devices for obtaining observation data related to a plurality of patients; a database for maintaining data related to a plurality of entities, wherein each of the plurality of entities maintains a logical relationship between one or more fields of the observation data stored in the database; and a computing device coupled to the database to identify relationships between different entities of the plurality of entities, generate metadata defining a graphical structure, the graphical structure maintaining relationships between different entities of the plurality of entities at a plurality of different logical layers, and store the metadata in the database.

[0019] In another embodiment, a method for querying a database is provided. The method includes: receiving an input query from a client device by a computing device coupled to the database; identifying a logical layer in a plurality of different logical layers of the database for searching by the computing device based at least in part on the input query; generating a query statement by the computing device based at least in part on the input query, the query statement being used to search the identified logical layer in the plurality of different logical layers of the database; using the query statement to query the identified logical layer of the database to obtain result data; and providing search results affected by the result data to the client device by the computing device.

[0020] In another embodiment, a database system is provided. The database system includes: a database for maintaining data related to a plurality of entities and metadata defining a graph structure, the graph structure maintaining relationships between different entities in the plurality of entities in each of a plurality of different logical layers; a client device coupled to a network for transmitting session input from a user of the client device; and a computing device coupled to the database and the network for receiving the session input from the client device, determining a logical layer in the plurality of different logical layers of the database for searching based at least in part on the session input, generating a query statement based at least in part on the session input, the query statement for searching the identified logical layer in the plurality of different logical layers of the database to obtain result data from the logical layer of the database, and providing search results affected by the result data to the client device via the network.

[0021] In another embodiment, a method for querying a database includes: providing a graphical user interface display at a client electronic device that prompts a user for conversational interaction; receiving conversational input from the user at the client electronic device; transmitting the conversational input from the client electronic device to a remote device over a network, wherein the remote device analyzes the conversational input based at least in part on the conversational input to identify a logical layer among a plurality of different logical layers of a database to search, and queries the identified database logical layer to obtain result data; and providing conversational search results within the graphical user interface display at the client electronic device in response to the conversational input, wherein the conversational search results are affected by the result data.

[0022] In another embodiment, an apparatus of an infusion device is provided. The infusion device includes: an actuation device operable to deliver a fluid to a user, the fluid affecting a physiological condition of the user; a communication interface for receiving measurement data indicative of the physiological condition of the user; a sensing device for obtaining background measurement data; and a control system coupled to the actuation device, the communication interface, and the sensing device to determine commands for autonomously operating the actuation device in a manner affected by the measurement data and the background measurement data, and autonomously operating the actuation device in accordance with the commands to deliver the fluid to the user.

[0023] In another embodiment, a method of operating an infusion device to adjust a patient's physiological condition is provided. The method includes: obtaining measurement data indicating a physiological condition from a first sensing device at the infusion device; determining a delivery command at the infusion device for autonomously operating an actuation device of the infusion device to deliver a fluid affecting the physiological condition to the patient; obtaining background measurement data from a second sensing device of the infusion device at the infusion device; adjusting the delivery command in a manner affected by the background measurement data to obtain an adjusted delivery command; and automatically operating the actuation device to deliver the fluid according to the adjusted delivery command.

[0024] In another embodiment, a method for monitoring a patient's physiological condition includes: obtaining, by a computing device, measurement data related to the patient's physiological condition from a sensing device; obtaining, by the computing device, medical record data associated with the patient from a database; determining, by the computing device, a risk score for a medical condition associated with the patient based at least in part on the measurement data, the medical record data, and one or more relationships between group measurement data and group medical record data; and initiating one or more actions at the computing device based at least in part on the risk score.

[0025] In another embodiment, a system is provided, comprising: a sensing device for obtaining measurement data related to a patient's physiological condition; a database for maintaining medical record data associated with the patient, group measurement data associated with multiple patients, and group medical record data associated with multiple patients; and a computing device communicatively coupled to the sensing device and the database, for determining a risk score for a medical condition associated with the patient based at least in part on the measurement data, the medical record data, and one or more relationships between the group measurement data and the group medical record data, and performing one or more actions when the risk score is greater than a threshold.

[0026] In another embodiment, a method for monitoring a patient includes: obtaining group glucose measurement data associated with multiple patients from a database; obtaining group medical record data associated with multiple patients from a database; determining a risk model for the medical condition for a subset of multiple patients having the medical condition based on a relationship between the group glucose measurement data and the group medical record data; obtaining sensor glucose measurement data of the patient from an interstitial glucose sensing device; obtaining medical record data associated with the patient from a database; using the risk model, determining a risk score for the medical condition associated with the patient based at least in part on the sensor glucose measurement data and the medical record data; and generating a treatment recommendation for the patient when the risk score is greater than a threshold.

[0027] In another embodiment, a method for managing a patient's physiological condition includes: obtaining, by a computing device, measurement data related to the patient's physiological condition from a sensing device; obtaining, by the computing device, medical record data associated with the patient from a database; classifying, by the computing device, the patient into patient groups based at least in part on the measurement data and the medical record data; obtaining, by the computing device, a plurality of different boost models associated with the patient groups, wherein each of the plurality of different boost models corresponds to a corresponding therapeutic intervention among a plurality of different therapeutic interventions, determining, by the computing device, a plurality of boost metric values ​​associated with the patient for the plurality of different therapeutic interventions based on the measurement data and the medical record data using the plurality of different boost models, and providing, by the computing device, an indication of a recommended therapeutic intervention to the patient based at least in part on the corresponding boost metric values ​​associated with the recommended therapeutic interventions.

[0028] In another embodiment, a method for managing a patient's physiological condition includes: obtaining, by a computing device coupled to a database, population measurement data associated with multiple patients from the database; obtaining, by the computing device, population medical record data associated with the multiple patients from the database; determining, by the computing device, a patient group including a subset of the multiple patients for modeling based on at least one of a subset of the population measurement data associated with the subset of the multiple patients and a subset of the population medical record data associated with the subset of the multiple patients; determining, by the computing device, a plurality of different lift models associated with the patient group for different therapeutic interventions based at least in part on one or more relationships between the subset of the population measurement data associated with the subset of the patients and the subset of the population medical record data; obtaining measurement data related to the patient's physiological condition from a sensing device; obtaining medical record data associated with the patient from a database; determining, using a plurality of different lift models, a plurality of lift metric values ​​associated with the patient based on the measurement data and the medical record data; selecting a recommended therapeutic intervention from the different therapeutic interventions based at least in part on the plurality of lift metric values; and providing an indication of the recommended therapeutic intervention to the patient.

[0029] In another embodiment, a system is provided, the system comprising: a sensing device for obtaining measurement data related to a patient's physiological condition; a database for maintaining medical record data associated with the patient and a plurality of different boost models associated with patient groups based on a relationship between group measurement data and group medical record data associated with a plurality of patients, wherein each of the plurality of different boost models corresponds to a respective therapeutic intervention among a plurality of different therapeutic interventions; and a computing device communicatively coupled to the sensing device and the database to classify the patient into patient groups based at least in part on the measurement data and the medical record data; determining a plurality of boost metric values ​​associated with the patient for a plurality of different therapeutic interventions based on the measurement data and the medical record data using the plurality of different boost models; and generating a user notification of a recommended therapeutic intervention for the patient based at least in part on the respective boost metric values ​​associated with the recommended therapeutic interventions.

[0030] In another embodiment, a method for managing a patient's physiological condition includes: obtaining, by a computing device, measurement data related to the patient's physiological condition from a sensing device; obtaining, by the computing device, medical record data associated with the patient from a database; obtaining, by the computing device, a plurality of different adherence models associated with a plurality of different treatment regimens, wherein each of the plurality of different adherence models corresponds to a corresponding treatment regimen among a plurality of different treatment regimens; determining, by the computing device, a plurality of adherence metric values ​​associated with the patient for a plurality of different treatment regimens based on the measurement data and the medical record data using the plurality of different adherence models; and providing, by the computing device, an indication of a recommended treatment regimen to the patient based at least in part on the corresponding adherence metric values ​​associated with the recommended treatment regimen.

[0031] In another embodiment, a method for managing a patient's physiological condition includes: obtaining, by a computing device, group medical record data of a plurality of patients to whom a treatment regimen is prescribed; obtaining, by a computing device, group medical claims data of a plurality of patients; obtaining, by a computing device, group measurement data of a plurality of patients; determining, by the computing device, an adherence model based at least in part on a relationship between the group medical claims data, the group medical record data, and the group measurement data; obtaining, by the computing device, measurement data related to the patient's physiological condition from a sensing device; obtaining, by the computing device, medical record data associated with the patient from a database; determining, by the computing device, an adherence metric value for the treatment regimen for the patient based at least in part on the measurement data and the medical record data using the adherence model; and recommending, by the computing device, a treatment regimen for the patient based on the adherence metric value.

[0032] In another embodiment, a system is provided, comprising: a sensing device for obtaining measurement data related to a patient's physiological condition; a database for maintaining medical record data associated with the patient and a plurality of different adherence models associated with a plurality of different treatment regimens, wherein each of the plurality of different adherence models corresponds to a respective treatment regimen of the plurality of different treatment regimens; and a computing device communicatively coupled to the sensing device and the database to determine, using the plurality of different adherence models, a plurality of adherence metric values ​​associated with the patient for the plurality of different treatment regimens based on the measurement data and the medical record data, and to generate a user notification of a recommended treatment regimen for the patient based at least in part on the respective adherence metric values ​​associated with the recommended treatment regimen.

[0033] This Summary is provided to introduce some concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] A more complete understanding of the present subject matter may be obtained by referring to the detailed description and claims when considered in conjunction with the following drawings, in which like reference numerals indicate similar elements throughout the drawings, which are illustrated for simplicity and clarity and are not necessarily drawn to scale.

[0035] Figure 1 An exemplary embodiment of a patient data management system is depicted;

[0036] Figure 2 is a combination of one or more exemplary embodiments Figure 1 A flowchart of a suitable implementation of an exemplary data management process for a patient data management system;

[0037] Figure 3 is a combination of one or more exemplary embodiments Figure 1 A flowchart of a suitable implementation of an exemplary query process for a patient data management system;

[0038] Figure 4 Describes the basis Figure 3 An exemplary embodiment of the query process is suitable for Figure 1 an exemplary graphical user interface (GUI) presented on a client electronic device in a patient data management system;

[0039] Figure 5 Describes the Figure 1 An exemplary graph data structure implemented at a logical database layer in a patient data management system of;

[0040] Figure 6 is a block diagram of an exemplary infusion system suitable for use with a fluid infusion device in one or more embodiments;

[0041] Figure 7 In one or more embodiments, it is suitable for Figure 6 A block diagram of an exemplary pump control system for use in an infusion device in an infusion system of;

[0042] Figure 8 is a flow chart of a suitable implementation of an exemplary forecasting process in conjunction with a patient data management system in one or more exemplary embodiments;

[0043] Fig. 9 Describes the basis Figure 8 An exemplary GUI display suitable for presentation on a client electronic device of an exemplary embodiment of a forecasting process;

[0044] Fig.10 Describes the combination Figure 8 A block diagram of an hourly forecast model used by an exemplary embodiment of a forecast process of;

[0045] Fig.11 is a flow chart of a suitable implementation of an exemplary ensemble prediction process in conjunction with a patient data management system in one or more exemplary embodiments;

[0046] Fig.12 Describes the basis Fig.11 an exemplary GUI display suitable for presentation on a client electronic device of an exemplary embodiment of an ensemble prediction process;

[0047] Fig.13 is a flow chart of a suitable implementation of an exemplary patient simulation process in conjunction with a patient data management system in one or more exemplary embodiments;

[0048] Figures 14 to 16 Describes the basis Fig.13 exemplary GUI displays suitable for presentation on a client electronic device of various exemplary embodiments of a patient simulation process;

[0049] Fig.17 is a flow chart of a suitable implementation of an exemplary risk management process in conjunction with a patient data management system in one or more exemplary embodiments;

[0050] Fig.18 is a flow chart of a suitable implementation of an exemplary enhancement suggestion process in conjunction with a patient data management system in one or more exemplary embodiments;

[0051] Fig.19is a flow chart of a suitable implementation of an exemplary compliance recommendation process in conjunction with a patient data management system in one or more exemplary embodiments;

[0052] Fig. 20 An exemplary embodiment of an infusion system is shown;

[0053] Fig.21 Shows the application Fig. 20 A plan view of an exemplary embodiment of a fluid infusion device of an infusion system;

[0054] Fig. 22 yes Fig.21 An exploded perspective view of a fluid infusion device;

[0055] Fig.23 is along Fig. 22 The line 23-23 in FIG. 23 shows the assembly of the reservoir inserted into the infusion set. Figure 21 to Figure 22 A cross-sectional view of a fluid infusion device;

[0056] Fig.24 is a block diagram of an exemplary patient monitoring system; and

[0057] Fig.25 Depicted is an embodiment of a computing device of a diabetes data management system adapted to communicate with a Figure 1 , Figure 6 , Fig. 20 and Fig.24 Any one or more systems and Figures 2 to 3 , Figure 8 , Fig.11 , Fig.13 and Figures 17 to 19 Any one or more processes may be used in combination. DETAILED DESCRIPTION

[0058] The following detailed description is merely illustrative in nature and is not intended to limit the embodiments of the subject matter or the application and use of these embodiments. As used herein, the word "exemplary" means "used as an example, instance or illustration". Any specific implementation described herein as exemplary is not necessarily to be understood as being more preferred or advantageous than other specific implementations. In addition, it is not intended to be bound by any express or implied theory presented in the aforementioned technical field, background technology, summary of the invention or the following detailed description.

[0059] For purposes of illustration, the subject matter herein may be described primarily in the context of infusion systems and devices that are configured to support monitoring and / or regulating glucose levels in a user in a personalized and / or context-dependent manner. That is, the subject matter described herein is not necessarily limited to glucose regulation or insulin infusion, and in practice may be implemented in an equivalent manner with respect to any number of other medications, physiological conditions, etc.

[0060] Although the subject matter described herein can be implemented in the context of any electronic device, the exemplary embodiments described below are implemented in conjunction with a medical device (such as a portable electronic medical device). Although there may be many different applications, the following description may focus primarily on a fluid infusion device (or infusion pump) as part of an infusion system deployment. For the sake of brevity, conventional techniques related to infusion system operation, insulin pump and / or infuser operation, and other functional aspects of the system (and individual operating components of the system) may not be described in detail herein. Examples of infusion pumps may be of the type described in, but not limited to, the following U.S. Patents: 4,562,751, 4,685,903, 5,080,653, 5,505,709, 5,097,122, 6,485,465, 6,554,798, 6,558,320, 6,558,351, 6,641,533, 6,659,980, 6,752,787, 6,817,990, 6,932,584, and 7,621,893, each of which is incorporated herein by reference. A fluid infusion device typically includes a motor or other actuating device for linearly displacing a plunger (or stopper) of a reservoir disposed within the fluid infusion device to deliver a dose of a fluid (such as insulin) to a user's body. In one or more exemplary embodiments, a delivery command (or dosage command) that controls the operation of the motor is determined based on a difference between a measured value of a physiological condition of the user's body and a target value using closed-loop control in a substantially autonomous manner and on a substantially continuous basis to adjust the measured value to the target value.

[0061] As follows Figures 1 to 5In one or more embodiments, historical observation patient data (e.g., measurement data, insulin delivery data, event log data, background data, etc.), electronic medical record data, and medical insurance claims data associated with multiple different patients are stored or otherwise maintained in a database and organized into multiple different logical layers. Each logical layer has its own associated directed graph data structure, which maintains the association or relationship between different entities in the logical layer. In this regard, an entity generally represents a container or logical grouping of fields, attributes, or other information that characterizes the entity. Therefore, an entity can maintain a logical association between one or more fields of a patient's historical observation data, a patient's electronic medical record data, and / or a patient's medical insurance claims data. For example, a patient identifier and one or more additional data fields associated with a single patient can be mapped to different entities in a specific logical database layer, which in turn is used as a node in a directed graph data structure associated with the logical database layer, and these nodes are linked to other nodes (or entities) in the logical database layer. Thus, similarities or commonalities between different patients or entities within a logical database layer can be exploited to establish links between different patients, lifestyle events, treatment regimens, patient outcomes, etc., which in turn can be used to provide improved recommendations related to the management of a given patient's condition, or otherwise improve the control, regulation, or understanding of a given patient's condition. Similarly, in some embodiments, similarities, commonalities, or causal relationships can be used to establish links between an entity within one logical database layer and another entity in a different logical layer, thereby establishing links or edges across logical database layers.

[0062] When corresponding data for an entity is loaded, created, or otherwise instantiated in a database, links or edges between different nodes (or entities) may be initially created. For example, when a new patient is introduced into a database system, a corresponding entity for the patient may be created within a logical database layer for the patient. Thereafter, the logical database layer may be searched to identify other entities that are related or associated with some aspect of the new entity. For example, if the entity for a new patient includes an identifier for a healthcare provider for the patient, a bidirectional link may be created to a node corresponding to an existing entity associated with the patient's healthcare provider (which may be in the same or different logical layer of the database).

[0063] In one or more exemplary embodiments, for each logical database layer, entities or nodes in a graph data structure associated with the layer are periodically analyzed to identify and create new causal or logical relationships between different nodes of the graph data structure. In one or more embodiments, the generated recursive neural network or other machine learning or artificial intelligence technology can periodically scan the nodes of the graph data structure to identify causal pairs and establish causal links (or edges) between such nodes of the graph data structure. For example, in response to a causal relationship engine using machine learning to identify causal relationships based on a common sequence of events occurring relative to one or more patients, a directed link can be created between entities corresponding to different types of meals and corresponding to different types of glucose excursion events (e.g., hyperglycemic events, hypoglycemic events, acute diabetic ketoacidosis, etc.). In one or more embodiments, the generated recursive neural network technology is applied in the following manner: starting randomly from different outcome nodes of interest, and tracing back to the links or edges of the node in an "irregular" manner to determine whether a specific pattern or sequence leads to the specific outcome node. In addition, query logs associated with queries executed on or at a particular logical database layer may be analyzed to detect repeated associations or query paths involving at least a threshold number of nodes, thereby establishing new edges between end nodes of the query paths to improve query performance. In some embodiments, new edges or relationships between entities or nodes in the graph data structure may also be manually established (e.g., based on new research, clinical evidence, data crawling and manual verification, or other external knowledge).

[0064] As follows Figures 3 to 5 As described in more detail in the context of , different logical database layers allow observed patient data, electronic medical record data, and medical insurance claims data to be effectively converted into different forms with different interrelationships between different data subsets, thereby adapting to different types of queries. In addition, the query results may be more personalized, or otherwise produce better patient outcomes, recommendations, or understanding of the patient's physiological condition. For example, natural language processing or other artificial intelligence techniques can be applied to an input query or search string to determine the intent or target associated with the input query, and then based on this, one or more logical database layers are identified to perform a search based on the query intent. Then, a query statement is constructed and executed on the identified logical database layer to obtain the result of the input query. In one or more embodiments, the initial query results are filtered or otherwise parsed based on information related to the current operating context (e.g., time of day, day of the week, geographic location, environmental conditions, etc.) to obtain context-related query results, and then these query results are output or otherwise provided in response to the input query.

[0065] As follows mainly Figures 3 to 7As described in more detail in the context of , in one or more embodiments, a medical device (such as an infusion device, a sensing device, a monitoring device, etc.) includes or otherwise supports a user interface capable of receiving a conversational input query, which is then parsed or otherwise analyzed at the medical device to obtain an input query to be analyzed, thereby identifying a logical database layer for the purpose of searching and generating a corresponding query statement. For example, in one or more embodiments, the medical device includes a microphone or a similar audio input device suitable for receiving an audio input from a user, which is then processed, parsed, or otherwise analyzed to identify the conversational input query in the audio input. The query results can be presented to the user in a conversational manner or otherwise in the context of a conversation or dialogue with the user within the user interface or the query results can be provided to the user in other ways. Therefore, a patient or user can be able to interact with and query a database system in a conversation, which can then be transformed to allow queries to be executed in a fast manner on different logical layers and provide personalized and context-related results, while also taking advantage of the interrelationships between different types of data and data subsets (e.g., different patients with similar overall statistical characteristics, different patients with similar medical histories, different patients with similar treatment regimens, etc.).

[0066] Diabetes Intelligence Network

[0067] Figure 1 An exemplary embodiment of a patient management system 100 is shown, which includes, but is not limited to, a computing device 102 coupled to a database 104, which is also communicatively coupled to one or more electronic devices 106 via a communication network 108 (e.g., the Internet, a cellular network, a wide area network (WAN), etc.). It should be understood that for purposes of illustration, Figure 1 A simplified representation of patient data management system 100 is shown and is not intended to limit the subject matter described herein in any way.

[0068] In an exemplary embodiment, the electronic device 106 includes one or more medical devices, such as an infusion device, a sensing device, a monitoring device, etc. In addition, the electronic device 106 may include any number of non-medical client electronic devices, such as a mobile phone, a smart phone, a tablet computer, a smart watch, or other similar mobile electronic devices, or any type of electronic device capable of communicating with the computing device 102 via the network 108, such as a laptop or notebook computer, a desktop computer, etc. One or more electronic devices 106 may include a display device (such as a monitor, a screen, or another conventional electronic display) or be coupled to a display device that is capable of graphically presenting data and / or information related to the patient's physiological condition. In addition, one or more electronic devices 106 also include a user input device (such as a keyboard, a mouse, a touch screen, a microphone, etc.) or are otherwise associated with a user input device that is capable of receiving input data and / or other information from a user of the electronic device 106.

[0069] In an exemplary embodiment, one or more electronic devices 106 transmit, upload or otherwise provide data or information to the computing device 102 for processing at the computing device 102 and / or storage in the database 104. For example, when the electronic device 106 is implemented as a sensing device, a monitoring device, or other device including a sensing element (which is inserted into the patient's body or otherwise worn by the patient to obtain measurement data indicating a physiological condition in the patient's body), the electronic device 106 can periodically upload or otherwise transmit the measurement data to the computing device 102. In other embodiments, when the electronic device 106 is implemented as an infusion device or similar device capable of delivering fluids or drugs to the patient, the electronic device 106 can periodically upload or otherwise transmit delivery data indicating the timing and amount of the fluid or drug delivered to the patient. In still other embodiments, the client electronic device 106 can be used by the patient to manually define, enter or otherwise record meals, activities or other events experienced by the patient, and then transmit, upload or otherwise provide these event log data to the computing device 102.

[0070] The computing device 102 generally represents a server or other remote device configured to receive data or other information from the electronic device 106, store or otherwise manage the data in the database 104, and analyze or otherwise monitor the data received from the electronic device 106 and / or stored in the database 104, as described in more detail below. In practice, the computing device 102 may reside in a location that is physically different and / or separate from the electronic device 106, for example, at a facility owned and / or operated by or associated with the manufacturer of one or more medical devices used in conjunction with the patient data management system 100. For purposes of illustration, but not limitation, the computing device 102 may alternatively be referred to herein as a server, a remote server, or variations thereof. The server 102 generally includes a processing system and a data storage element (or memory) capable of storing programming instructions for execution by the processing system, which, when read and executed, causes the processing system to create, generate, or otherwise facilitate an application or software module configured to perform or otherwise support the processes, tasks, operations, and / or functions described herein. Depending on the embodiment, the processing system can be implemented using any suitable processing system and / or device, such as one or more processors, central processing units (CPUs), controllers, microprocessors, microcontrollers, processing cores and / or other hardware computing resources configured to support the operation of the processing system described herein. Similarly, the data storage element or memory can be implemented as a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic or optical mass storage, or any other suitable non-transitory short-term or long-term data storage or other computer-readable medium and / or any suitable combination thereof.

[0071] In an exemplary embodiment, the database 104 is used to store or otherwise maintain historical observation patient data 120, electronic medical record data 122, and medical insurance claims data 124 for a plurality of different patients. In this regard, a subset of patients having associated data in one of the data sets 120, 122, 124 may also have associated data in another of the data sets 120, 122, 124. That is, some but not necessarily all patients associated with one of the data sets 120, 122, 124 may be common to another of the data sets 120, 122, 124. In an exemplary embodiment, the database 104 also stores or maintains metadata 126 for characterizing or otherwise defining directed graph data structures corresponding to different logical layers within the database 104. In this regard, the graph metadata 126 may define nodes (or entities) that make up a graph data structure associated with a particular logical database layer, wherein each of these nodes (or entities) is mapped to one or more fields of the data sets 120, 122, 124. Additionally, the graph metadata 126 characterizes or defines edges or links between nodes within a graph data structure associated with a particular logical database layer that establish logical or causal relationships between nodes within that logical database layer. In various embodiments, a node (or entity) may exist in multiple different logical database layers, or a node (or entity) in one logical database layer may link to another node (or entity) in a different logical database layer.

[0072] In the illustrated embodiment, the server 102 implements or otherwise executes a data management application 110 that receives or otherwise obtains data from the electronic device 106, stores the received data in the database 104, and generates or otherwise creates entities that logically associate different fields of the stored data 120, 122, 124. The data management application 110 also generates or otherwise creates graph metadata 126 to maintain relationships between different entities in the database 104, as described below. Figures 2 to 5 In the illustrated embodiment, the server 102 also implements or otherwise executes a query management application 112 that receives or otherwise obtains input queries from one or more electronic devices 106 and generates, executes, or otherwise performs corresponding query statements on one or more different logical layers of the database 104 in response to the corresponding input queries to obtain results provided to the corresponding electronic devices 106, as described below in Figures 2 to 5 described in more detail in the context of .

[0073] Still reference Figure 1In an exemplary embodiment, the historical observation data 120 maintained in the database 104 includes historical measurement data associated with a particular patient (or patient identifier) ​​indicating the patient's physiological condition relative to time (e.g., historical blood glucose values, historical interstitial fluid glucose values, etc.), historical delivery data indicating the amount of fluid or medication delivered to the patient relative to time (e.g., historical meal or correction bolus, basal dose or other automatically delivered amount, etc.), historical meal data and / or other event log data associated with the patient, historical contextual data related to the measurement data, delivery data, event log data, etc. For example, the server 102 may receive measurement data values ​​associated with a particular patient (e.g., sensor glucose measurements, acceleration measurements, etc.) obtained using a sensing element from a medical device via the network 108, and the server 102 stores or otherwise maintains the historical measurement data in the database 104 associated with the patient as patient data 120 (e.g., using one or more unique patient identifiers). In addition, the server 102 may also receive meal data or other event log data from or via the client device 106 that may be input by the patient or otherwise provided (e.g., via a client application at the client device 106), and store or otherwise maintain historical meal data and other historical events or activity data associated with the patient in the database 104. In this regard, meal data includes, for example, a time or timestamp associated with a particular meal event, a meal type or other information indicating the composition or nutritional characteristics of the meal, and an indication of the size associated with the meal. In an exemplary embodiment, the server 102 also receives historical fluid delivery data (e.g., insulin delivery dose amounts and corresponding timestamps) corresponding to a basal dose or bolus dose of a fluid delivered to the patient by the infusion device 106. The server 102 may also receive geographic location data and possibly other context data associated with the electronic device 106 that provides the patient data 120, and store or otherwise maintain historical operational context data associated with a particular patient. In this regard, one or more of the devices 106 may include a global positioning system (GPS) receiver, or similar module, component, or circuit capable of outputting or otherwise providing data characterizing the geographic location of the respective device 106 in real-time.

[0074] Electronic medical record (EMR) data 122 generally includes information associated with one or more identifiers for a given patient within an EMR data set indicating the following: medical diagnoses or medical conditions that the patient has been diagnosed with, drugs or medications administered or taken by the patient, prescription information, changes in therapy for the patient, laboratory results or measurements of the patient's physiological condition, the patient's immunization record, microbiology results or other observations related to the patient, healthcare utilization information (e.g., hospitalizations, emergency room visits, outpatient visits, etc.), overall demographic information associated with the patient (e.g., age, income, education, location, gender), past medical procedures, clinical observations or other habitual behavioral information (e.g., smoking, drinking, etc.), family medical history, physician notes and care plans, etc. The EMR data 122 may also include data about healthcare providers associated with various aspects of the patient's medical record, the patient's insurance information, etc. In various embodiments, the server 102 may receive or obtain the EMR data 122 from another server computing device, another database different from the database 104 (e.g., by copying from another database), individual computing devices associated with healthcare providers, patients, etc. The claims data 124 generally includes information associated with one or more identifiers for a given patient within a claims data set, and related to a health insurance claim submitted by or on behalf of the patient, including charge information, prescriptions fulfilled or refilled by the patient, etc. Similar to the EMR data 122, the server 102 may receive or obtain the claims data 124 from another server computing device, another database different from the database 104 (e.g., by copying from another database), various computing devices associated with a healthcare provider, a patient, a pharmacy, etc. In an exemplary embodiment, the claims data 124 includes medical, pharmaceutical, and obstetric-related claims data, including corresponding diagnoses, procedures, prescription codes, charges (e.g., net amount plus allowed amount, etc.), etc.

[0075] Figure 2 Describes a method suitable for use by a computing device such as Figure 1 The exemplary data management process 200 implemented in the server 102 of the patient data management system 100 of FIG. 200. The various tasks performed in conjunction with the data management process 200 may be performed by hardware, firmware, software executed by processing circuitry, or any combination thereof. For purposes of illustration, the following description relates to the above-mentioned combined Figure 1elements mentioned. In practice, portions of the data management process 200 may be performed by different elements of the patient data management system 100; however, for purposes of explanation, the data management process 200 may be described primarily in the context of implementation at or by the server 102 and / or the data management application 110. It should be understood that the data management process 200 may include any number of additional or alternative tasks, which need not be performed in the order shown and / or which may be performed simultaneously, and / or the data management process 200 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. Furthermore, portions of the data management process 200 may be omitted from an actual implementation of the data management process 200 as long as the intended overall functionality remains unchanged. Figure 2 One or more tasks shown and described in the context of.

[0076] In an exemplary embodiment, the data management process 200 is performed, facilitated, or otherwise supported by the data management application 110 at the server 102 to generate graph metadata 126 for different logical layers supported by the database 104. For example, in one embodiment where the database system 104 maintains data 120, 122, 124 related to diabetic patients, the database 104 supports five different logical layers: a patient layer, a lifestyle layer, a treatment layer, a diabetes management layer, and a diabetes knowledge layer. The patient layer contains a subset of the patient data 120 and EMR data 122 related to each patient, including, but not limited to, historical patient blood glucose measurements, information characterizing historical glucose excursion events, and information characterizing complications or improvements to the corresponding patient's physiological condition. In this regard, the graph metadata 126 can indicate which fields of the patient data 120 and EMR data 122 associated with the individual patient should be mapped or otherwise used for the nodes of the patient layer graph data structure and the corresponding edges or links between those nodes. The patient layer can be queried to obtain information related to the patient's health history, such as glucose measurements, excursion events, year-on-year improvements, comorbidities, complications, etc. The lifestyle layer includes event log data and other possible subsets of patient data 120 and EMR data 122 related to the respective individual patients. The treatment layer includes subsets of data 120, 122, 124 that indicate which drugs, medications, or other treatments are associated with the respective patients and may include, for example, indications of what type of medical device the patient may be using to manage or monitor his or her treatment (e.g., infusion device, continuous glucose monitoring device, etc.) and the costs associated with the patient's treatment. The diabetes management layer includes subsets of data 120, 122, 124 that support maintaining relationships between different individuals or entities represented within the data sets 120, 122, 124, including patients, healthcare providers, physicians, payers, hospitals, etc. The diabetes knowledge layer contains subsets of data 120, 122, 124 that support queries for patient-independent general knowledge, such as queries related to specific physiological conditions or diagnoses (e.g., type 1 diabetes, type 2 diabetes, etc.), pharmacodynamics of insulin or other fluids, medications, or medications, excursion events, meal types, etc.

[0077] For each logical database layer, the data management process 200 shown periodically scans or otherwise analyzes the nodes or entities in the graph data structure associated with the corresponding logical database layer to identify the causal relationship between the entities in the logical database layer (tasks 202, 204). In one or more embodiments, the data management application 110 implements or otherwise performs a causal relationship analysis based on machine learning to find repeated causal pairings of nodes in the graph data structure. For example, a timestamp or other temporal relationship between a meal event entity and a glucose drift event entity associated with a specific patient or across multiple different patients can be used to identify the causal relationship between meal events and glucose drift events, and establish a causal link between meal events and glucose drift event entities in the lifestyle layer. In response to discovering a relationship between previously unconnected nodes or entities in the graph data structure associated with the corresponding logical database layer, the data management process 200 creates or otherwise generates updated graph metadata characterizing the relationship between the identified nodes, and the updated graph metadata is stored or otherwise maintained in a database associated with the logical database layer (tasks 206, 208). In this regard, the data management application 110 updates the graph metadata 126 associated with a particular logical database layer to create new directed edges or links between previously unconnected nodes or entities identified as having a causal relationship within the logical database layer.

[0078] As an example, the data management application 110 can detect a pattern in which meals with a fat content exceeding a threshold value (e.g., exceeding 50 grams) result in a hyperglycemic excursion event with a duration exceeding a threshold duration (e.g., exceeding 45 minutes), and thereby establish a directed link or edge between one or more meal event nodes with a fat content exceeding the threshold value and a corresponding hyperglycemic excursion outcome node. A weight or other quantitative value can be assigned to the newly created edge, which corresponds to or otherwise reflects the strength of the relationship between the nodes (e.g., based on a probabilistic analysis of the incidence of the outcome). As another example, it can be determined that for a particular group of patients who share certain characteristics, exercising more than a threshold number of times per week (e.g., 3 or more times per week) results in increased insulin sensitivity, thereby establishing a directed link or edge between certain exercise event nodes and an increased insulin sensitivity outcome node, wherein the weight corresponds to the relative probability that the corresponding exercise event causes an increase in insulin sensitivity.

[0079] Still reference Figure 2In an exemplary embodiment, the data management process 200 also analyzes a query log associated with the corresponding logical database layer to identify relationships between previously unconnected nodes or entities within the logical database layer based on results of previously executed queries. In this regard, the database 104 can store or otherwise maintain a query log, wherein in response to executing a query statement, a corresponding log entry is created that maintains an association between the logical database layer being queried and the query path resulting from executing the query statement (e.g., the order of nodes and edges traversed in the logical database layer during execution of the query statement).

[0080] In an exemplary embodiment, for each logical database layer, the illustrated data management process 200 periodically analyzes a query log associated with the logical database layer to identify logical relationships between nodes or entities within the logical database layer based on repeated queries that traverse those nodes or entities (tasks 210, 212). For example, in one or more embodiments, the data management application 110 analyzes the query log associated with a particular logical database layer to identify or retrieve query paths that traverse more than a threshold number of nodes or entities within the database layer (e.g., more than 3 nodes). Of the subset of query paths that traverse more than the threshold number of nodes, the data management application 110 identifies repeated query paths that have common end nodes and establishes logical relationships between those end nodes within the logical database layer. In this regard, the data management process 200 creates or otherwise generates updated graph metadata that establishes logical relationships between previously unconnected terminal nodes, and stores or otherwise maintains the updated graph metadata in a database associated with the logical database layer (tasks 214, 216). In this manner, the data management application 110 creates new edges or links between end nodes of repeated query paths that have a common end node and traverse more than a threshold number of nodes.

[0081] By creating or otherwise establishing relationships between previously unconnected nodes within a graph data structure of a particular logical database layer with the aid of data management process 200, subsequent queries of that logical database layer may be conducted or executed more efficiently, or otherwise provide improved results reflecting possible causal and / or logical relationships between nodes of the graph data structure. In an exemplary embodiment, data management process 200 is performed for each different logical database layer, and data management process 200 may be repeated periodically (e.g., daily, weekly, monthly, etc.) to continuously analyze and update the relationships between nodes or entities within the corresponding logical database layer.

[0082] Figure 3 Describes a method suitable for querying a database having multiple different logical layers, such as Figure 1104 in the patient data management system 100). For purposes of illustration, the query process 300 may be described herein primarily in the context of input queries received from a human user or patient in a conversational format. However, it should be understood that the query process 300 is not limited to conversational input queries received from a user, and that the query process 300 may be implemented in an equivalent manner for queries that are neither submitted nor initiated by a user and are not provided in a conversational format. The various tasks performed in conjunction with the query process 300 may be performed by hardware, firmware, software executed by a processing circuit, or any combination thereof. For purposes of illustration, the following description relates to the query process 300 described above in conjunction with the present invention. Figure 1 elements mentioned. In practice, portions of the query process 300 may be performed by different elements of the patient data management system 100; however, for purposes of explanation, the query process 300 may be described primarily in the context of implementation at or by the server 102 and / or the query management application 112. It should be understood that the query process 300 may include any number of additional or alternative tasks, which need not be performed in the order illustrated and / or which may be performed simultaneously, and / or the query process 300 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. Furthermore, portions of the query process 300 may be omitted from an actual implementation of the query process 300 as long as the intended overall functionality remains unchanged. Figure 3 One or more tasks shown and described in the context of.

[0083] In the illustrated embodiment, the query process 300 receives or otherwise obtains an input query from a patient or other user (task 302). For example, a patient can interact with or otherwise manipulate a user interface associated with a client application on an electronic device 106 to create an input query, which is then transmitted or otherwise provided to a server 102 via a network 108. In one or more embodiments, the input query is implemented as a conversational string of words or text provided to the server 102. In this regard, a user can create or provide an input query in a free form or unstructured manner using natural language rather than predefined grammar. For example, in one or more embodiments, the electronic device 106 includes an audio input device and a speech recognition engine or vocabulary that supports parsing or otherwise decomposing a conversational voice or audio input by a user of the device 106 into a corresponding text representation to be provided to the server 102. In various embodiments, a conversational input query can be received silently, or alternatively, a user can manipulate the device 106 to select or otherwise activate a graphical user interface (GUI) element that enables or initiates the query process 300. For example, in one or more embodiments, the query process 300 may be initiated in response to a user selecting a GUI element for a search feature, digital assistant, or similar feature supported by a client application at device 106. In response, the client application at device 106 may generate or otherwise provide a GUI display or other GUI element, prompting the user to indicate what he or she wants to know or ask. Thereafter, the user may enter a conversational string (e.g., via voice, typing, swiping, touch, or any other suitable input method), where a textual representation of the conversational input query is provided by device 106 to server 102 over network 108.

[0084] In an exemplary embodiment, the query process 300 also receives or otherwise obtains background information associated with the input query from the client electronic device that provides the input query (task 304). The operational background information provided together with the input query characterizes the current operating state or environment when the query is input. For example, associated with the submitted input query, the client device 106 may also transmit or otherwise provide background information related to the operation of the client device 106, such as the current location of the client device 106, the current local time at the location of the client device 106 and the current day of the week, the current environmental conditions at the location of the client device 106, etc. In addition, in some embodiments, the client device 106 may also provide information indicating the current physiological condition of the user and / or the current operating state of the infusion device or other medical device associated with the user. For example, together with the input query, the client device 106 may transmit or otherwise provide one or more of the following: a current or recent glucose measurement associated with the user, an indication of whether to suspend insulin delivery by the infusion device associated with the user, a current or recent insulin delivery to the user, a current or recent heart rate measurement related to the user, an acceleration measurement, or other measurements of the activity level associated with the user, etc.

[0085] The illustrated query process 300 continues to identify or otherwise determine which one or more logical database layers should be queried based at least in part on the input query, and then generate or otherwise construct one or more corresponding query statements to be executed on the identified logical database layers based at least in part on the input query (tasks 306, 308). In this regard, the query management application 112 at the server 102 can analyze the input query to identify or otherwise determine the possible intent or purpose of the query, and then determine the logical database layer to be queried based on the intent or purpose of the query. In some embodiments, when determining which logical database layer should be queried, the query management application 112 at the server 102 can also analyze the background information associated with the input query and the content of the input query. Once the logical layer to be queried is identified, the query management application 112 at the server 102 analyzes the content of the input query to obtain parameters or criteria to be used for the query, and then uses these parameters or criteria to generate or otherwise construct query statements for execution on the identified logical database layer. Additionally, in some embodiments, the query management application 112 at the server 102 may also utilize operational context information associated with the input query for one or more parameters or criteria when constructing a query statement.

[0086] After constructing the query statement, the query process 300 executes or otherwise initiates execution of the constructed query statement on the identified logical database layer to obtain the results of the input query from the identified logical database layer (task 310). In this regard, when the constructed query statements are linked or dependent on each other, the query management application 112 at the server 102 can initiate a first query statement on a first logical layer of the database 104 to obtain results for the intermediate query statement, which is in turn utilized by a second query statement executed on a different logical layer of the database 104. For example, the results obtained from querying one logical database layer can be used as parameters or criteria in subsequent query statements on a different logical database layer. In addition, in some embodiments, the results obtained from querying one logical database layer can be filtered, processed, analyzed, or otherwise optimized to determine parameters or criteria for use in subsequent query statements on different logical database layers, as described below. Figure 4 described in more detail in the context of .

[0087] In order to execute a query statement, the database 104 utilizes the graph metadata 126 to traverse the nodes or entities within the logical database layer of the query to obtain the results of the query statement based on the established edges or links between the nodes or entities within the logical database layer. It should be noted that, with the help of a weighted directed graph data structure for maintaining data in the database 104, by supporting point-based index references that do not require complex table scan sequences, the response time for executing a query statement at the database 104 is generally less than that of a conventional database that relies on table scans based on primary keys and / or foreign keys. In an exemplary embodiment, as described above, the query path that details the nodes or entities and corresponding edges traversed during the execution of the query statement is also stored or otherwise maintained in a query log at the server 102 or one of the databases 104 associated with the logical database layer of the query.

[0088] In one or more exemplary embodiments, the query process 300 filters the initial query results based on the operational context information associated with the input query, and then generates output query results based on the filtered query results in response to the received input query (task 312). In this regard, the initial query results can be analyzed relative to the current operational context associated with the input query to select or otherwise identify a subset of the initial query results that are most relevant to one or more aspects of the current operational context. The query management application 112 at the server 102 can select from the initial query results obtained by executing the query statement: a subset of information that is most likely to be related to the current location of the client device 106, the current local time at the location of the client device 106, the day of the week, the current environmental conditions at the location of the client device 106, the current physiological condition of the patient, the current operational state of an infusion device or other medical device, etc. For example, the query management application 112 can select one of the initial query results that is closest to the current location of the client device 106 or within a threshold distance of the current location. As another example, the query management application 112 may select one of the initial query results that is most likely to produce the best patient outcome based on the patient's current glucose level, the current operating state of the patient's infusion device (e.g., delivery paused, reservoir depleted, etc.).

[0089] The query process 300 generates or otherwise constructs a response to the input query based on the filtered query results, and then presents or otherwise provides the query response in response to the input query (tasks 314, 316). For example, in one or more embodiments, the query management application 112 generates a session query response using the filtered query results, and then transmits the session query response to the query client device 106 for presentation or reproduction in the context of a session that includes the session input query.

[0090] Figure 4 Describes the combinable Figure 3 The query process 300 Figure 11. An exemplary embodiment of a graphical user interface (GUI) display 400 presented at a query client device 406 (e.g., one of the devices 106) in the patient data management system 100 of FIG. 10. The GUI display 400 includes a conversation box or one or more similar GUI elements that prompts a user to conversationally interact with the client device 106, 406 to query the database 104. In the illustrated embodiment, a patient using the query client device 106, 406 utilizes an input device or user interface at the client device 106, 406 to input or otherwise provide a conversation input query. In response, an application at the client device 106, 406 updates the GUI display 400 to graphically depict a textual representation of the conversation input query 402 received by the client device 106, 406. The application at the client device 106, 406 transmits, submits, or otherwise provides the conversation input query text to the query management application 112 at the server 102 for execution.

[0091] As above Figure 3 As described in the context of , the query management application 112 analyzes the conversation input query text "What should I eat now?" to determine the intent or purpose of the input query (e.g., intent = finding food), the subject of the input query (e.g., subject = patient identifier), and any other time or context parameters contained in or associated with the input query (e.g., time = now). Based on identifying the subject of the input query as the patient, the query management application 112 can determine that the lifestyle logic layer of the database 104 should be queried to obtain lifestyle information related to the patient. In this regard, the query management application 112 can construct an initial query statement for querying the lifestyle logic layer of the database 104 using the patient's unique identifier to retrieve lifestyle information related to the patient. For example, executing a query on the lifestyle logic layer of the database 104 can return information indicating the current or recent type of diet that the patient is consuming (e.g., low carbohydrate), information related to the patient's exercise habits or other recent activities performed by the patient, and / or other contextual information that may describe the patient's lifestyle. Additionally, based on identifying the subject of the input query as a patient, the query management application 112 may also query the patient logical layer of the database 104 to identify other patients that are similar to the patient that is the subject of the input query (e.g., based on edges or links in the graph metadata 126 for the patient logical layer that link those other patients to the current patient via more than a threshold number of common nodes or entities).

[0092] Using the lifestyle information obtained by querying the lifestyle logic layer and identifiers of other patients similar to the subject patient, the query management application 112 generates or otherwise constructs a query statement for querying the patient logic layer to obtain meal records or other meal information associated with a subset of similar patients who have similar lifestyle information associated therewith (e.g., patients with similar exercise or activity behaviors, geographic locations, etc.) and whose meal outcomes are good (e.g., no hypoglycemic events or hyperglycemic events or other excursion events occurred after the meal for patients with similar lifestyle backgrounds, post-meal glucose is within a threshold range of the patient's target glucose value, etc.). After obtaining information about meals consumed by similar patients with positive outcomes from the patient logic layer, the query management application 112 may generate or otherwise construct a query statement for querying the lifestyle logic layer to identify a subset of those meals that best match or are most closely associated with the patient's lifestyle information (e.g., meals associated with a low-carbohydrate diet or other meals of a low-carbohydrate diet related thereto, etc.). In one or more exemplary embodiments, the query statement may also take into account the patient's current operating context (e.g., meals within a threshold distance of the current location of the client device 106, 406, etc.). In still other embodiments, the current operating context is used to filter or otherwise exclude query results and identify meal results that best match the querying patient's current operating context and lifestyle.

[0093] After obtaining the best-matched meal result for the patient's lifestyle and current operating context from the query lifestyle logic layer, and achieving positive outcomes for one or more similar patients, the query management application 112 generates or otherwise constructs a query response that includes or incorporates the meal result in a conversational form. In this regard, in one or more embodiments, based on the identified meal type and the current location associated with the input query, the query management application 112 queries a database of restaurant information, the restaurant information including geographic location information and menu data associated with a plurality of restaurants, so as to identify a restaurant that is closest to or near the current location of the query device 106, 406 and provides items that match or correspond to the identified meal. The query management application 112 can then generate a query response text that indicates the best-matched identified restaurant and menu item with the meal result obtained from the query database 104. The query management application 112 transmits or otherwise provides the conversational query response text to the query client device 106, 406 for presentation at the client device 106, 406. The application at the client device 106 , 406 updates the GUI display 400 to graphically depict a textual representation of a session query response 404 received by the client device 106 , 406 from the server 102 in the context of a session that includes the session input query 402 .

[0094] Figure 5 An exemplary graphical representation of a portion of a graph data structure 500 corresponding to a subset of a patient logical layer in the database 104 is depicted, the graphical representation depicting relationships between different patients that may be related to a query subject patient based on the graph metadata 126. In this regard, Figure 5 A node 502 within the patient logic layer associated with an inquiry patient is depicted. Based on the graph metadata 126, the inquiry patient node 502 is associated with a plurality of different entity nodes 504 within the patient logic layer, wherein those entity nodes 504 correspond to different fields or subsets of the data 120, 122, 124 in the database 104 that are associated with the inquiry patient and that are mapped to the respective nodes based on the graph metadata 126 of the patient logic layer. Additionally, the graph metadata 126 of the patient logic layer may also define edges or links between the entity node 504 associated with the inquiry patient 502 and one or more other patient nodes 506 associated with different patients that have similar values ​​for their related fields or subsets of the data 120, 122, 124 in the database 104 that are mapped to those entity nodes 504. In some embodiments, weights may be assigned to edges between an entity node 504 and a similar patient node 506 based on differences or similarities between values ​​of associated fields or subsets of the query patient's data 120, 122, 124 that map to that node 504 and values ​​of those fields or subsets of the data 120, 122, 124 associated with the respective patients whose patient nodes 506 are linked to the respective entity node 504. Thus, the number of edges between respective pairs of related patient nodes 502, 506 and the respective weights assigned to those edges may be used to calculate or otherwise determine a metric indicative of the relative similarity between a query subject associated with a patient node 502 and a different patient associated with one of the patient nodes 506.

[0095] As above Figure 4 As described in the context of , when a query statement is executed on the patient logical layer, the graph metadata 126 associated with the patient logical layer in the database 104 can be utilized to obtain patient identifiers associated with the patient nodes 506 that are most similar to the query subject (e.g., based on a similarity measure characterizing the relationship between the corresponding pair of patient nodes 502, 506). The patient identifiers associated with the similar patient nodes 506 can then be used to query other logical layers of the database 104 to obtain information indicating the meals, activities, medications, therapies, etc. of these patients and how such variables affect the physiological conditions of these patients (e.g., glucose measurements, excursion events, etc.), so as to generate recommendations or otherwise provide query results that are most likely to achieve the best outcome with respect to the physiological conditions of the query subject patients.

[0096] In some embodiments, the nodes 504 may reside in a different logical layer of the database 104 than the patient nodes 502, 506, wherein corresponding pairs of patient nodes 502, 506 have at least a threshold number of nodes 504 that are common or shared within another logical layer and that are used to establish a relationship between the corresponding pairs of patient nodes 502, 506, or otherwise classify the pair of patient nodes 502, 506 into a common group or cohort, as described in more detail below.

[0097] Cognitive Pump

[0098] Reference now Figure 6 According to one or more exemplary embodiments, the infusion device 602 in the infusion system 600 is used as a Figure 1 The server 102 in the patient data management system 100 queries the electronic device 106 of the database 104. In this regard, the infusion device 602 can receive the session user input and capture simultaneous, concurrent or time-related operational context information associated with the session user input, and according to Figure 3 The query process 300 provides the corresponding session input query text and associated context information to the query management application 112 at the server 102 .

[0099] In an exemplary embodiment, the infusion system 600 is also capable of automatically or autonomously controlling or otherwise regulating a physiological condition within the user's body 601 to a desired (or target) value, or otherwise maintaining the condition within a range of acceptable values. In one or more exemplary embodiments, the regulated condition is sensed, detected, measured, or otherwise quantified by a sensing device 604 (e.g., sensing device 604) communicatively coupled to the infusion device 602. However, it should be noted that in alternative embodiments, the condition regulated by the infusion system 600 may be related to a measurement obtained by the sensing device 604. That is, for clarity and illustration purposes, the present subject matter may be described herein in the context of a glucose sensing device in which the sensing device 604 is implemented as a glucose sensing device that senses, detects, measures, or otherwise quantifies a user's glucose level, which is regulated in the user's body 601 by the infusion system 600.

[0100] In an exemplary embodiment, the sensing device 604 includes one or more interstitial glucose sensing elements that generate or otherwise output an electrical signal (alternatively referred to herein as a measurement signal) having a signal characteristic that is related to, affected by, or otherwise indicates a relative interstitial fluid glucose level in the user's body 601. The output electrical signal is filtered or otherwise processed to obtain a measurement indicating the user's interstitial fluid glucose level. In an exemplary embodiment, a blood glucose meter 630 such as a finger-pricking device is used to directly sense, detect, measure, or otherwise quantify blood glucose in the user's body 601. In this regard, the blood glucose meter 630 outputs or otherwise provides a measured blood glucose value, which can be used as a reference measurement result for calibrating the sensing device 604 and converting the measurement indicating the user's interstitial fluid glucose level into a corresponding calibration blood glucose value. For illustrative purposes, the calibration blood glucose value calculated based on the electrical signal output by one or more sensing elements of the sensing device 604 may alternatively be referred to as a sensor glucose value, a sensing glucose value, or a variation thereof.

[0101] In an exemplary embodiment, the infusion system 600 also includes one or more additional sensing devices 606, 608, which are configured to sense, detect, measure or otherwise quantify characteristics of the user's body 601 that indicate the condition of the user's body 601. In this regard, in addition to the glucose sensing device 604, one or more auxiliary sensing devices 606 can be worn, carried or otherwise associated with the user's body 601 to measure user (or user activity) characteristics or conditions that can affect the user's blood glucose level or insulin sensitivity. For example, a heart rate sensing device 606 can be worn on the user's body 601 or otherwise associated with the user's body to sense, detect, measure or otherwise quantify the user's heart rate, which in turn can indicate movement (and its intensity) that may affect the glucose level or insulin response in the user's body 601. In yet another embodiment, another invasive, interstitial or subcutaneous sensing device 606 can be inserted into the user's body 601 to obtain a measurement result of another physiological condition that can indicate movement (and its intensity), such as, for example, a lactate sensor, a ketone sensor, etc. Depending on the embodiment, the auxiliary sensing device 606 may be implemented as a separate component worn by the user, or alternatively, the auxiliary sensing device 606 may be integrated with the infusion device 602 or the glucose sensing device 604.

[0102] The illustrated infusion system 600 also includes an acceleration sensing device 608 (or accelerometer) that can be worn on or otherwise associated with the user's body 601 to sense, detect, measure, or otherwise quantify the acceleration of the user's body 601, which in turn can indicate movement of the body 601 or some other condition that may affect the user's insulin response. Figure 6 602, but in alternative embodiments, the acceleration sensing device 608 may be integrated with other sensing devices 604, 606 on the user's body 601, or the acceleration sensing device 608 may be implemented as a separate independent component worn by the user.

[0103] In an exemplary embodiment, the infusion device 602 also includes one or more environmental sensing devices 650 to sense, detect, measure, or otherwise quantify the current operating environment surrounding the infusion device 602. In this regard, the environmental sensing device 650 may include one or more of a temperature sensing device (or thermometer), a humidity sensing device, a pressure sensing device (or barometer), etc. In an exemplary embodiment, the infusion device 602 also includes a location sensing device 660 to sense, detect, measure, or otherwise quantify the current geographic location of the infusion device 602, such as a global positioning system (GPS) receiver.

[0104] In the illustrated embodiment, the pump control system 620 generally represents the electronics and other components of the infusion device 602 that control the operation of the fluid infusion device 602 in accordance with the desired infusion delivery program in a manner affected by the sensed glucose value indicating the current glucose level in the user's body 601. For example, to support a closed-loop operating mode, the pump control system 620 maintains, receives, or otherwise obtains a target or commanded glucose value, and automatically generates or otherwise determines a dosage command for operating an actuator such as a motor 632 to displace the plunger 617 and deliver insulin to the user's body 601 based on the difference between the sensed glucose value and the target glucose value. In other operating modes, the pump control system 620 may generate or otherwise determine a dosage command that is configured to maintain the sensed glucose value below an upper glucose limit, above a lower glucose limit, or other values ​​within a desired range of glucose values. In practice, the infusion device 602 may store or otherwise maintain a target value, one or more upper and / or lower glucose limits, one or more insulin delivery limits, and / or one or more other glucose thresholds in a data storage element accessible to the pump control system 620.

[0105] Still reference Figure 6, target glucose values ​​and other threshold glucose values ​​utilized by the pump control system 620 may be received from an external component or may be input by a user via a user interface element 640 associated with the infusion device 602. In practice, the one or more user interface elements 640 associated with the infusion device 602 typically include at least one input user interface element, such as, for example, a button, a keypad, a keyboard, a knob, a joystick, a mouse, a touch panel, a touch screen, a microphone or other audio input device, etc. In addition, the one or more user interface elements 640 include at least one output user interface element for providing notifications or other information to the user, such as, for example, a display element (e.g., a light emitting diode, etc.), a display device (e.g., a liquid crystal display, etc.), a speaker or other audio output device, a tactile feedback device, etc. It should be noted that although Figure 6 The one or more user interface elements 640 are shown as being separate from the infusion device 602, but in practice, the one or more user interface elements 640 can be integrated with the infusion device 602. Furthermore, in some embodiments, in addition to and / or in lieu of the one or more user interface elements 640 being integrated with the infusion device 602, the one or more user interface elements 640 are integrated with the sensing device 604. The user can manipulate the one or more user interface elements 640 as desired to operate the infusion device 602 to deliver a correction bolus, adjust a target value and / or threshold, modify a delivery control scheme or operating mode, and the like.

[0106] Still reference Figure 6 In the illustrated embodiment, the infusion device 602 includes a motor control module 612 coupled to a motor 632, which is operable to displace a plunger 617 in a reservoir and provide a desired amount of fluid to a user's body 601. In this regard, displacement of the plunger 617 enables a fluid (such as insulin) capable of affecting a physiological condition of the user to be delivered to the user's body 601 via a fluid delivery path (e.g., via tubing of an infusion set). A motor driver module 614 is coupled between an energy source 618 and the motor 632. The motor control module 612 is coupled to the motor driver module 614, and the motor control module 612 generates or otherwise provides a command signal to operate the motor driver module 614 to provide current (or power) from the energy source 618 to the motor 632 to displace the plunger 617 in response to receiving a dosage instruction from the pump control system 620 indicating a desired amount of fluid to be delivered.

[0107] In an exemplary embodiment, the energy source 618 is implemented as a battery housed within the infusion device 602 that provides direct current (DC) power. In this regard, the motor driver module 614 generally represents a combination of circuits, hardware, and / or other electrical components that are configured to convert or otherwise transform the direct current provided by the energy source 618 into an alternating current signal that is applied to each phase of the stator winding of the motor 632, which causes current to flow through the stator winding, thereby generating a stator magnetic field and causing the rotor of the motor 632 to rotate. The motor control module 612 is configured to receive or otherwise obtain a command dose from the pump control system 620, convert the command dose into a command translation displacement of the plunger 617, and command, signal, or otherwise operate the motor driver module 614 to rotate the rotor of the motor 632 by an amount that produces the command translation displacement of the plunger 617. For example, the motor control module 612 can determine the amount of rotation of the rotor required to produce a translation displacement of the plunger 617 that achieves the command dose received from the pump control system 620. Based on the current rotational position (or orientation) of the stator relative to the rotor indicated by the output of the rotor sensing device 616, the motor control module 612 determines the appropriate sequence of AC electrical signals to be applied to the phases of the stator windings, which should cause the rotor to rotate a determined amount of rotation relative to its current position (or orientation). In an embodiment where the motor 632 is implemented as a BLDC motor, the AC electrical signals commutate the phases of the stator windings at the appropriate orientation of the rotor poles relative to the stator and in an appropriate sequence to provide a rotating stator magnetic field that rotates the rotor in the desired direction. The motor control module 612 then operates the motor driver module 614 to apply the determined AC electrical signals (e.g., command signals) to the stator windings of the motor 632 to achieve the desired fluid delivery to the user.

[0108] When the motor control module 612 is operating the motor driver module 614, current flows from the energy source 618 through the stator windings of the motor 632 to generate a stator magnetic field that interacts with the rotor magnetic field. In some embodiments, after the motor control module 612 operates the motor driver module 614 and / or the motor 632 to achieve the command dose, the motor control module 612 stops operating the motor driver module 614 and / or the motor 632 until a subsequent dose command is received. In this regard, the motor driver module 614 and the motor 632 enter an idle state, during which the motor driver module 614 effectively disconnects or separates the stator windings of the motor 632 from the energy source 618. In other words, when the motor 632 is idle, current does not flow from the energy source 618 through the stator windings of the motor 632, so the motor 632 does not consume power from the energy source 618 in the idle state, thereby improving efficiency.

[0109] Depending on the embodiment, the motor control module 612 may be implemented or realized with a general purpose processor, microprocessor, controller, microcontroller, state machine, content addressable memory, application specific integrated circuit, field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. In an exemplary embodiment, the motor control module 612 includes or otherwise accesses a data storage element or memory, which includes any kind of random access memory (RAM), read only memory (ROM), flash memory, registers, hard disk, removable disk, magnetic or optical mass storage, or any other short-term or long-term storage medium or other non-transitory computer readable medium capable of storing programming instructions executed by the motor control module 612. When read and executed by the motor control module 612, the computer executable programming instructions cause the motor control module 612 to perform or otherwise support the tasks, operations, functions, and processes described herein.

[0110] It should be understood that the subject matter described herein is for illustrative purposes and is not intended to limit the subject matter described in any way. Figure 6 is a simplified representation of the infusion device 602. In this regard, depending on the implementation, some features and / or functions of the sensing device 604 may be implemented by the pump control system 620 or otherwise integrated into the pump control system, or vice versa. Similarly, in practice, features and / or functions of the motor control module 612 may be implemented by the pump control system 620 or otherwise integrated into the pump control system, or vice versa. In addition, the features and / or functions of the pump control system 620 may be implemented by control electronics located in the fluid infusion device 602, while in alternative embodiments, the pump control system 620 may be implemented by a remote computing device that is physically different and / or separate from the infusion device 602 (e.g., a mobile computing device coupled to the infusion device 602 via a personal area network, etc.).

[0111] Figure 7 According to one or more embodiments, a Figure 6 6. The pump control system 700 of the embodiment of the present invention is an exemplary embodiment of the pump control system 700 of the pump control system 620 in the embodiment of the present invention. The illustrated pump control system 700 includes, but is not limited to, a pump control module 702, a communication interface 704, and a data storage element (or memory) 706. The pump control module 702 is coupled to the communication interface 704 and the memory 706, and the pump control module 702 is appropriately configured to support the operations, tasks, and / or processes described herein. In various embodiments, the pump control module 702 is also coupled to one or more user interface elements (e.g., user interface 640) for receiving user input (e.g., target glucose value or other glucose threshold) and providing notifications, alarms, or other treatment information to the user.

[0112] The communication interface 704 generally represents the hardware, circuitry, logic, firmware, and / or other components of the pump control system 700 that are coupled to the pump control module 702 and are configured to support communication between the pump control system 700 and one or more of the various sensing devices 604, 606, 608, 650, 660. In this regard, the communication interface 704 may include or otherwise be coupled to one or more transceiver modules capable of supporting wireless communication between the pump control system 620, 700 and the external sensing devices 604, 606. For example, the communication interface 704 may be used to wirelessly receive sensor measurements or other measurement data from each external sensing device 604, 606 in the infusion system 600. In other embodiments, the communication interface 704 may be configured to support wired communication to / from the external sensing devices 604, 606. In various embodiments, the communication interface 704 may also support communication with a remote server (e.g., server 102) or another electronic device in the infusion system (e.g., to upload sensor measurements, receive control information, etc.).

[0113] The pump control module 702 generally represents the hardware, circuitry, logic, firmware, and / or other components of the pump control system 700 that are coupled to the communication interface 704 and the sensing devices 604, 606, 608, 650, 660 and configured to determine dosage commands for operating the motor 632 to deliver fluid to the body 601 based on measurement data received from the sensing devices 604, 606, 608, 650, 660 and to perform various additional tasks, operations, functions, and / or operations described herein. For example, in an exemplary embodiment, the pump control module 702 implements or otherwise executes a command generation application 710 that supports one or more autonomous operating modes and calculates or otherwise determines dosage commands for operating the motor 632 of the infusion device 602 in the autonomous operating mode based at least in part on current measurements of the condition of the user's body 601. For example, in the closed-loop operating mode, the command generation application 710 may determine a dosage command for operating the motor 632 to deliver insulin to the user's body 601 to adjust the user's blood glucose level to a target reference glucose value based at least in part on the current glucose measurement value most recently received from the sensing device 604. In various embodiments, the dosage command may also be adjusted or otherwise influenced by background measurement data, i.e., measurement data that characterizes, quantifies, or otherwise indicates the simultaneous or concurrent operating context of the dosage command, such as environmental measurement data obtained from the environmental sensing device 650, current location information obtained from the GPS receiver 660, and / or other context information that characterizes the current operating environment of the infusion device 602. In addition, the command generation application 710 may generate a dosage command for a bolus that is manually initiated or otherwise directed by the user via a user interface element.

[0114] In one or more exemplary embodiments, the pump control module 702 also implements or otherwise executes a prediction application 708 (or prediction engine) that is configured to estimate or otherwise predict future physiological conditions and potentially other future activities, events, operating environments, etc. in a personalized, patient-specific (or patient-specific) manner. In this regard, in some embodiments, the prediction engine 708 is cooperatively configured to interact with the command generation application 710 to support adjusting the dosage command or controlling information indicating the manner in which the dosage command is generated in a predictive or prospective manner. In this regard, in some embodiments, based on the correlation between the current or recent measurement data and the current operating context relative to the historical data associated with the patient, the prediction engine 708 can forecast or otherwise predict the patient's future glucose level at different times in the future, and correspondingly adjust or otherwise modify the value of one or more parameters utilized by the command generation application 710 in a manner that takes into account the predicted glucose level when determining the dosage command, for example, by modifying the parameter value at a register or location in the memory 706 referenced by the command generation application 710. In various embodiments, the prediction engine 708 can predict meals or other events or activities in which the patient is likely to engage, and output or otherwise provide an indication of how the patient's predicted glucose level is likely to be affected by the predicted event, which indication can then be reviewed or considered by the patient to proactively adjust his or her behavior, and / or used to adjust in a personalized manner the manner in which dosing instructions are generated to regulate glucose in a manner that takes into account the patient's behavior.

[0115] In one or more exemplary embodiments, the pump control module 702 also implements or otherwise executes a conversational interaction application 712 configured to support conversational interaction with a patient or other user. For example, the conversational interaction application 712 can generate or otherwise provide a GUI display on a display device 640 associated with the infusion device 602, the GUI display including a conversation box prompting a user to conversationally interact with the infusion device 602. In this regard, in one or more embodiments, the conversational interaction application 712 can generate a GUI display, such as the GUI display 400, that prompts a user to conversationally query or search the database system 104. The conversational interaction application 712 can also support conversational monitoring or management of a patient's physiological condition, as described above in Figure 3 to Figure 4 described in the context of and below in Figures 13 to 16 In one or more exemplary embodiments, the pump control module 702 also implements or otherwise executes a recommendation application 714 (or recommendation engine) that is configured to support providing treatment recommendations to the patient, as described below in Figures 17 to 19 described in more detail in the context of .

[0116] Still reference Figure 7 Depending on the embodiment, the pump control module 702 may be designed to perform the functions described herein as a general purpose processor, microprocessor, controller, microcontroller, state machine, content addressable memory, application specific integrated circuit, field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof to implement or realize. In this regard, the steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be directly embodied in hardware, in firmware, in a software module executed by the pump control module 702, or in any practical combination thereof. In an exemplary embodiment, the pump control module 702 includes or otherwise accesses a data storage element or memory 706, which may be implemented using any type of non-transitory computer readable medium capable of storing programming instructions executed by the pump control module 702. The computer executable programming instructions, when read and executed by the pump control module 702, cause the pump control module 702 to implement or otherwise generate the application programs 708, 710, 712, 714 and perform the tasks, operations, functions, and processes described herein.

[0117] It should be understood that the subject matter described herein is for illustrative purposes and is not intended to limit the subject matter described in any way. Figure 7 is a simplified representation of the pump control system 700. For example, in some embodiments, the features and / or functionality of the motor control module 612 may be implemented by or otherwise integrated into the pump control system 700 and / or pump control module 702, such as by converting dose commands into corresponding motor commands via the command generation application 710, in which case a separate motor control module 612 may not be present in embodiments of the infusion device 602.

[0118] Glucose Prediction and Forecast

[0119] In one or more exemplary embodiments, the patient-specific forecast model for physiological condition is determined based on the historical data related to the patient, and is used to predict the future value or level of physiological condition based at least in part on the current measured value of current operating background and physiological condition. In addition, historical event data and associated background information can be used to predict one or more future events of different future times within the forecast range based at least in part on current measured data and / or current operating background (e.g., the current time of the day, the current date of the week, the current geographical location, etc.), which can then be input into the patient-specific forecast model so that the forecast value or level of physiological condition are adjusted to reflect the forecast event at the appropriate time in the future. Although this paper describes this theme in the context of glucose forecast and prediction, this theme is not necessarily limited to glucose levels and may be implemented in an equivalent manner to forecast or predict other physiological conditions of an individual.

[0120] In an exemplary embodiment, a patient-specific glucose forecasting model is determined that allows the patient's glucose level to be predicted for future discrete time intervals. For illustrative purposes, this article describes this theme in the context of hourly forecasts that allow the patient's glucose level to be predicted on an hourly basis; however, it should be noted that the subject matter described herein is not limited to hourly forecasts and can be used for different forecast time intervals (e.g., every 15 minutes, every 30 minutes, every 4 hours, etc.).

[0121] Figure 8 Describes a method suitable for use by a computing device such as Figure 1 The exemplary forecasting process 800 implemented in the server 102 or client electronic device 106 of the patient data management system 100 of FIG. The various tasks performed in conjunction with the forecasting process 800 may be performed by hardware, firmware, software executed by a processing circuit, or any combination thereof. For illustrative purposes, the following description relates to the above-mentioned forecasting process 800. Figure 1 and Figure 6 to Figure 7 mentioned elements. In practice, portions of the forecasting process 800 may be performed by different elements of the patient data management system 100 or the infusion system 600 (e.g., the server 102, one or more electronic devices 106, the infusion device 602, and / or the pump control system 620, 700). It should be understood that the forecasting process 800 may include any number of additional or alternative tasks, which need not be performed in the order illustrated and / or which may be performed simultaneously, and / or the forecasting process 800 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. In addition, the forecasting process 800 may be omitted from an actual implementation of the forecasting process 800 as long as the intended overall functionality remains unchanged. Figure 8 One or more tasks shown and described in the context of.

[0122] The illustrated embodiment of the forecasting process 800 initializes or otherwise begins by retrieving or otherwise obtaining historical data associated with a patient of interest to be modeled, and using the historical data associated with the patient to develop, train, or otherwise determine a forecast model for the patient (tasks 802, 804). In one or more exemplary embodiments, for an individual patient within the patient data management system 100, the server 102 periodically retrieves or otherwise obtains historical patient data 120 associated with the patient from the database 104, and analyzes the relationships between different subsets of the historical patient data 120 to create a patient-specific forecast model associated with the patient. Depending on the embodiment, the patient-specific forecast model can be stored on the database 104 associated with the patient and utilized by the server 102 to determine a glucose forecast for the patient (e.g., in response to a request from a client device 106) and provide the resulting glucose forecast to the client device 106 for presentation to the user. In other embodiments, the server 102 pushes, provides, or otherwise transmits a patient-specific prediction model to one or more electronic devices 106 associated with the patient (e.g., infusion device 602) for implementing and supporting glucose forecasting at the end-user device (e.g., via prediction engine 708).

[0123] In one or more exemplary embodiments, a recurrent neural network is used to create an hourly neural network unit that is trained based on a subset of historical patient data corresponding to the hourly interval on multiple different days prior to model development to predict the average glucose level of the patient associated with the corresponding hourly interval. For example, in one embodiment, for each hourly interval in a day, a corresponding long short-term memory (LSTM) unit (or cell) is created, wherein the LSTM unit outputs the average blood glucose value for the hourly interval based on the subset of historical patient data corresponding to the hourly interval and the variables of one or more LSTM units before the current LSTM unit. For example, the LSTM unit associated with the 1-2PM time interval is configured to calculate the average glucose value for the patient in the 1-2PM time frame based on the subset of historical patient data timestamped or associated with the 1-2PM time frame and the input and / or output of one or more previous LSTM units (e.g., the average blood glucose value of the patient in the 12-1PM time frame output by the 12-1PM LSTM unit, the relevant portion of the subset of historical patient data timestamped or associated with the 12-1PM time frame, etc.).

[0124] For each LSTM unit, machine learning can be utilized to determine corresponding equation, function or model to calculate the average glucose value of the patient in the time interval based at least in part on the patient's historical insulin delivery data, historical meal data and historical exercise data during the time interval. In this regard, the model of specific hourly interval can characterize or map the average glucose value of the sensor of hourly interval being modeled by the insulin delivery data during the hourly interval, the meal data during the hourly interval, the exercise data during the hourly interval and the average glucose value of the previous hourly interval. In addition, hourly model can consider historical auxiliary measurement data (for example, historical acceleration measurement data, historical heart rate measurement data, etc.), historical drug data or other historical event log data, historical geographic location data, historical environmental data, and / or other history or background data that may be related to the average glucose level of the patient during the time interval or predict this average glucose level. Therefore, because different variables produce more or less influence on the patient's glucose level in one day, each function or equation associated with corresponding LSTM unit can appropriately increase or decrease the weighting or emphasis of specific input variables to the average glucose value calculated by corresponding LSTM unit. It should be noted that any number of different machine learning techniques may be utilized to determine which input variables predict the current patient of interest and the current hourly interval of the day, such as artificial neural networks, genetic programming, support vector machines, Bayesian networks, probabilistic machine learning models or other Bayesian techniques, fuzzy logic, combinations of heuristic derivations, and the like.

[0125] The forecasting process 800 continues to receive, retrieve or otherwise obtain recent patient data, identify or otherwise obtain a current operating context associated with the patient, and predict the patient's future behavior based on the recent patient data and the current operating context (tasks 806, 808, 810). In this regard, a prediction model for future insulin delivery, future meals, future exercise events, and / or future medication doses can be determined that characterizes or maps a particular combination of one or more of current (or recent) sensor glucose measurement data, auxiliary measurement data, delivery data, geographic location, meal data, exercise data, patient behavior or activity, etc. to a value representing a current probability or likelihood of a particular event and / or a current value associated with the particular event or activity (e.g., predicted meal size, predicted exercise duration and / or intensity, predicted bolus amount, etc.). Thus, the forecasting process 800 can obtain current or recent sensor glucose measurements associated with the patient, as well as data or information quantifying or characterizing recent insulin delivery, meals, exercise, and other events, activities, or behaviors that the user may have performed within a previous time interval (e.g., within the previous 2 hours) from one or more of the sensing devices 604, 606, 608, the infusion device 602, and / or the database 104. The forecasting process 800 can also obtain data or information quantifying or characterizing the current or recent operating context associated with the infusion device 602 from one or more of the sensing devices 650, 660, the infusion device 602, and / or the database 104.

[0126] Based on current and recent patient measurement data, insulin delivery data, meal data and exercise data, as well as the current time of day, the current date of the week and / or other current or recent environmental data, the forecast process 800 determines the event probability and / or characteristics of the future hourly time interval. For example, for each future hourly time interval, the forecast process 800 can determine the meal probability and / or predicted meal amount during the future hourly time interval, which can be used as the input of the LSTM unit in the hourly interval. Similarly, the forecast process 800 can determine the predicted insulin delivery amount, predicted exercise probability and / or predicted exercise intensity or duration, predicted drug dosage, etc. in each corresponding future hourly time interval, which is based on the relationship between the historical patient data and background data before the previous instance of these events occurred. Some examples of predicting patient behavior or activity are described in U.S. Patent Application Serial No. 15 / 847,750, which is incorporated herein by reference.

[0127] Still reference Figure 8After predicting the future patient behavior that may affect the patient's future glucose level, the forecast process 800 continues to calculate or otherwise determine the forecast glucose level of the future hourly interval based at least in part on the current or most recent glucose measurement data and the predicted future behavior, and generate or provide a graphical representation of the forecast glucose level associated with different future hourly intervals (tasks 812, 814). Based on the current time of the day, a forecast model for the next hourly interval of the day can be selected and utilized to calculate the forecast glucose level of the hourly interval based at least in part on the most recent sensor glucose measurement value and the predicted meal, exercise, insulin delivery and / or drug dosage of the next hourly interval of the day. For example, the current sensor glucose measurement value obtained in the current hourly interval and the previous sensor glucose measurement value can be averaged or otherwise combined to obtain the average sensor glucose measurement value of the current hourly interval, which can be input into the forecast model for the next hourly interval of the day. The forecast model is then utilized to calculate the forecast average glucose value of the next hourly interval of the day based on the average sensor glucose measurement value of the current hourly interval and the predicted patient behavior of the next hourly interval. The predicted average glucose value for the next hourly interval may then be input into the prediction model for the subsequent hourly interval for use in calculating the predicted glucose value for that subsequent hourly interval based on its associated predicted patient behavior, among other things.

[0128] Fig. 9 Describe the exemplary GUI display 900 including glucose forecast area 902, which includes a graphical representation of the forecast glucose level of the patient associated with the subsequent hourly interval in a day. In the shown GUI display 900, the graphical representation 904 of the patient's recent sensor glucose measurement data is presented near the glucose forecast area 902. In an exemplary embodiment, the sensor glucose measurement value display area 904 includes a line graph or a line graph of the patient's historical sensor glucose measurement data, which has a visually distinguishable coverage area of ​​the target range of the sensor glucose measurement value of the patient. Depending on the embodiment, the GUI display 900 can be presented on the display device 640 associated with the infusion device 602 or on another electronic device 102, 106 in the patient data management system 100. In one or more embodiments, the forecast process 800 is performed so as to generate a forecast area 902 on the GUI display 900 in response to the patient's selection being configured to cause the GUI element presenting the forecast area 902 or otherwise requesting the presentation of glucose forecast (e.g., by requesting glucose forecast via the conversational interactive application 712 conversationally).

[0129] refer to Figures 8 to 9In one or more exemplary embodiments, based on the current time of day (e.g., 9:45AM), the current sensor glucose measurement (e.g., 110 mg / dL), and possibly other recent patient data (e.g., a recent meal, exercise, or bolus) and / or the current operating context, the forecasting process 800 calculates or otherwise determines predicted patient behavior for the 10AM hourly interval and subsequent hourly intervals for which the patient's glucose level is to be forecast. Fig.10 Depicts a graphical representation of a portion of a recurrent neural network comprising hourly LSTM cells configured to compute using predicted patient behavior at future hourly intervals and the patient's current sensor glucose measurement data Fig. 9 . In this regard, the average sensor glucose value for the current interval 1001 is input into the 10AM hourly interval LSTM cell 1002 along with the predicted patient behavior 1003 for the 10AM hourly interval (e.g., the predicted amount of carbohydrates consumed, insulin delivered, exercise, medication, etc., by the patient during the period from 10AM to 11AM). Depending on the implementation, the current interval sensor glucose value 1001 can be implemented as the current or most recent sensor glucose measurement value (e.g., 110 mg / dL), the average of current and previous sensor glucose measurements obtained during the current interval (e.g., the average of sensor glucose measurements timestamped between 9:00AM and 9:45AM), or another sensor glucose value calculated based at least in part on the current sensor glucose measurement value. For example, the current sensor glucose measurement and other recent activity can be used to predict the patient's glucose level for the remainder of the current time interval (e.g., from 9:45AM to 10AM), which can then be averaged, weighted, or otherwise combined with the average of previous sensor glucose measurements obtained during the current time interval (e.g., from 9AM to 9:45AM) to obtain an estimated average sensor glucose measurement for the current time interval.

[0130] Based on the average sensor glucose value of the current interval 1001 and the predicted patient behavior 1003 for the 10 AM interval, the LSTM cell 1002 calculates or otherwise determines an average glucose value 1005 associated with the 10 AM interval using a forecast model for the 10 AM hourly interval, which forecast model is determined based on a subset of the patient's historical patient data associated with the 10 AM hourly interval (e.g., task 804). It should be noted here that in one or more embodiments, the amount of active insulin or carbohydrates does not necessarily need to be calculated or input into the LSTM cell 1002 within the 10 AM interval, because active insulin, carbohydrates and / or substitutes can be obtained from previous LSTM cells and scaled, reduced, discarded or otherwise adjusted according to the model associated with the LSTM cell 1002. The average glucose value 1005 associated with the 10 AM interval (e.g., 115 mg / dL) is displayed on the forecast area 902 associated with the 10 AM hourly interval of the GUI display 900. The predicted 10AM glucose value 1005 is also input to the 11AM hourly interval LSTM cell 1004 along with the predicted patient behavior 1007 for the 11AM hourly interval (e.g., the predicted amount of carbohydrates consumed, insulin delivered, exercise, medication, etc., by the patient during the period from 11AM to 12PM). The LSTM cell 1004 calculates or otherwise determines an average glucose value 1009 associated with the 11AM interval using a forecast model for the 11AM hourly interval that is determined based on a subset of the patient's historical patient data associated with the 11AM hourly interval (e.g., task 804). The predicted glucose value 1009 for the 11AM interval (e.g., 110 mg / dL) is displayed on the forecast area 902 associated with the 11AM hourly interval of the GUI display 900 and is input to the 12PM hourly interval LSTM cell 1006 for determining a predicted glucose value for the 12PM interval (e.g., 100 mg / dL), and so on.

[0131] In one or more exemplary embodiments, the predicted glucose values ​​in the glucose forecast area 902 are displayed or otherwise presented with visually distinguishable characteristics that indicate the relationship of the individual predicted glucose values ​​relative to one or more threshold values. Fig. 9In the illustrated embodiment, forecast glucose values ​​within a target range for a patient's glucose value (e.g., between 80 mg / dL and 140 mg / dL) are presented using a visually distinguishable characteristic that indicates that these values ​​are normal, expected, or other acceptable values ​​(e.g., green), wherein forecast glucose values ​​outside the target range are presented using a different visually distinguishable characteristic that indicates that these values ​​may be problematic or unwelcome (e.g., red). In this regard, for the illustrated embodiment, forecast glucose values ​​associated with the 4 PM hourly interval and the 5 PM hourly interval in the glucose forecast area 902 may be presented in red to indicate that they are below a lower threshold of the patient's target glucose range, the lower threshold indicating that a possible hypoglycemic event is predicted for the patient at or around those times, while forecast glucose values ​​prior to 4 PM in the glucose forecast area 902 may be presented in green to indicate that the patient's glucose is forecast to be within the target range for the next 6 hours.

[0132] In one or more embodiments, glucose forecast area 902 is scrollable or can be otherwise adjusted to allow patients or users to view future further forecast glucose values. For example, in one or more embodiments, glucose forecast area 902 is scrollable or can be otherwise adjusted to allow patients or users to view the forecast glucose values ​​for the next 24 hours. In addition, it should be noted that in some embodiments, the forecast glucose value in glucose forecast area 902 can be dynamically updated in real time in response to the patient's current sensor glucose measurement value, current operating background or other real-time behaviors or active changes performed by the patient.

[0133] Reference now Fig.11 According to one or more embodiments, an ensemble prediction process 1100 may be performed to determine an ensemble prediction for a patient's physiological condition as a combination of prediction values ​​determined using a plurality of different prediction models. Because different prediction models may utilize different input variables, different prediction ranges, and / or different formulas or techniques to determine future glucose values, an ensemble prediction of a patient's glucose level may better reflect potential changes in the patient's future glucose level than would otherwise be possible by relying on any individual prediction model. In this regard, predicted hourly glucose values ​​determined according to forecast process 800 may be weighted or otherwise combined with predicted glucose values ​​for the patient determined using other prediction models to obtain an ensemble prediction of the patient's glucose level with respect to time that reflects the relative reliability or accuracy of the respective prediction models with respect to time.

[0134] The various tasks performed in conjunction with the ensemble prediction process 1100 may be performed by hardware, firmware, software executed by a processing circuit, or any combination thereof. Figure 1 and Figure 6 to Figure 7 mentioned elements. In practice, portions of the ensemble prediction process 1100 may be performed by different elements of the patient data management system 100 or the infusion system 600 (e.g., the server 102, one or more electronic devices 106, the infusion device 602, and / or the pump control system 620, 700). It should be understood that the ensemble prediction process 1100 may include any number of additional or alternative tasks, which need not be performed in the order illustrated and / or which may be performed simultaneously, and / or the ensemble prediction process 1100 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. In addition, the ensemble prediction process 1100 may be omitted from an actual implementation of the ensemble prediction process 1100 as long as the intended overall functionality remains unchanged. Fig.11 One or more tasks shown and described in the context of.

[0135] The ensemble prediction process 1100 begins by retrieving or otherwise obtaining historical data associated with a particular patient, and developing, training, or otherwise determining a plurality of different patient-specific glucose prediction models based on the patient's historical data (tasks 1102, 1104). In this regard, in addition to determining the glucose prediction model described above in the context of the prediction process 800, one or more other models for the patient may be determined based on the patient's historical data 120 that predicts the patient's future glucose levels in different ways (e.g., using different algorithms or modeling techniques, etc.), based on different input variables with different levels of time granularity (e.g., in minutes, in hours, etc.), etc. It should be noted that, in practice, many different types of prediction models may be utilized, and the subject matter described herein is not intended to be limited to any particular type of model, technique, or method, or combination thereof, for predicting glucose levels.

[0136] For example, in one or more embodiments, in addition to the patient-specific neural network-based forecasting model, the patient's historical data is also used to determine the autoregressive integrated moving average (ARIMA) model for predicting future glucose levels. In this regard, the ARIMA model predicts future glucose levels based on the periodic pattern in the patient's historical data, which may be related to different events and / or operating contexts. In an exemplary embodiment, the ARIMA model is configured to determine the patient's predicted glucose values ​​with future increments, and these increments correspond to the sampling rate associated with the glucose sensing device 604 and / or the patient's historical sensor glucose measurement data. For example, in one embodiment, the glucose sensing device 604 provides new or updated sensor glucose measurements every 5 minutes, and the ARIMA model is configured to determine the predicted glucose values ​​at intervals of 5 minutes in the future. In one or more exemplary embodiments, machine learning or similar techniques are used to determine which combination of historical transport data, historical auxiliary measurement data, historical event log data, historical geographic location data, and other historical or contextual data is associated with or predictive of historical sensor glucose measurement data, and then based on the set of input variables and a previous subset of historical sensor glucose measurement values, a corresponding ARIMA model for calculating or predicting future sensor glucose measurement values ​​is determined. In this regard, the trajectory of the previous subset of historical sensor glucose measurement values ​​is combined with concurrent or previous events or operational contexts that historically correlate with or predict changes in the patient's sensor glucose level, thereby affecting the predicted future sensor glucose measurement values ​​determined using the model. In one embodiment, training of the autoregressive component of the ARIMA model attempts to determine the ability of the patient's glucose level to regress on its own, while the moving average component of the ARIMA model attempts to compensate for slow background drifts in the patient's glucose level.

[0137] In one or more embodiments, the patient's historical data is also used to determine a patient-specific physiological model for predicting future glucose levels, which is in an attempt to simulate the pharmacodynamics and pharmacokinetics of the patient and compensate for differences within and between personnel. Similar to the ARIMA model, the patient-specific physiological model can be configured to determine the patient's predicted glucose value with a future increment corresponding to a sampling rate, and the sampling rate is associated with the glucose sensing device 604 and / or the patient's historical sensor glucose measurement data. That is, the patient-specific physiological model may determine the predicted glucose value in a different manner from the ARIMA model and / or based on input variables different from the input variables used by the ARIMA model. For example, in one or more embodiments, one or more patient-specific physiological parameters (e.g., the rate of occurrence of glucose, insulin action, etc.) are determined for the patient based on the relationship between the patient's historical sensor glucose measurement data, historical meal data, historical delivery data, historical push data, etc. Based on the patient's current or recent sensor glucose measurement value, the current insulin in the body, the recent meal data, etc., the physiological model uses the patient-specific physiological parameters to predict future sensor glucose measurement values. In this regard, the output of the patient-specific physiological parameters represents the patient's expected glucose level based on the patient's historical physiological response given the current amount of insulin in the body and / or the amount of carbohydrate to be metabolized by the patient.

[0138] Still reference Fig.11 In an exemplary embodiment, after determining or otherwise obtaining a plurality of different patient-specific glucose prediction models, the collective prediction process 1100 proceeds to identify or otherwise obtain a current operating context for the patient, and based on the current operating context, calculate or otherwise determine reliability metrics associated with different patient-specific glucose prediction models for different prediction horizons before a current time of day (tasks 1106, 1108). In this regard, the current time of day, the current day of the week, the current geographic location, the current network address and / or network connection type, and / or other context data associated with the device 106, 602 associated with the patient are identified or otherwise obtained. Based on the current operating context, a subset of the patient history data 120 corresponding to the current operating context is obtained, and the subset is used to determine one or more accuracy or reliability metrics associated with different glucose prediction models. For example, if the current operating context indicates that it is 8AM on Wednesday and the patient is at home, previous subsets of the patient's historical data 120 that have associated timestamps at or around 8AM on Wednesday and / or have associated geographic locations that correspond to the patient's home geographic location can be obtained and then used to determine the reliability of different glucose prediction models.

[0139] For each prediction model, appropriate input variables are obtained from the relevant subset of the patient's historical data, and then the calculated glucose values ​​output by the model are compared with the patient's historical sensor glucose measurements at the corresponding time to obtain a reliability measure for the model. For example, the mean absolute difference, standard deviation, or other statistical measure can be calculated in the following manner: a set of output values ​​from the glucose prediction model corresponding to the prediction range after a certain time point (e.g., the predicted glucose values ​​four hours after the 8AM reference point) is compared with the corresponding historical glucose measurements (e.g., the patient's sensor historical glucose measurements four hours after the 8AM reference point on Wednesday). In one or more embodiments, the reliability measure is determined for hourly intervals, for example, by calculating the mean absolute difference in the first hour after the prediction time (e.g., using the value corresponding to the time frame from 8AM to 9AM), the mean absolute difference in the second hour after the prediction time (e.g., using the value corresponding to the time frame from 9AM to 10AM), etc. In this regard, the reliability measure associated with each specific prediction model can vary depending on the specific prediction range or time window before the current prediction time.

[0140] Based on the reliability metrics associated with the different prediction models, the ensemble prediction process 1100 calculates or otherwise determines weighting factors associated with the outputs of the different patient-specific glucose prediction models for the different prediction horizons prior to the current time of day, and then uses these weighting factors to calculate or otherwise determine the ensemble predicted glucose values ​​within the different prediction horizons as a weighted average of the outputs of the different patient-specific glucose prediction models (tasks 1110, 1112). In this regard, based on the relationship between the reliability metrics among the different patient-specific glucose prediction models for a particular prediction horizon, time window, or sampling time, the weighting factors may be assigned to the different models accordingly to increase the influence of one or more more reliable models on the ensemble predicted glucose values ​​within the prediction horizon.

[0141] For example, if the reliability of the patient's ARIMA model for the second hour before the current forecast time (e.g., 9AM to 10AM time frame) is fifty percent higher than the reliability of the patient's hourly forecast model, a weighting factor can be assigned to the predicted glucose value output by the ARIMA model at the second hour that is fifty percent higher than the weighting factor assigned to the hourly forecast model. The ensemble forecast value for the forecast range (i.e., the second hour before the current time) can then be determined as the weighted average of the 5-minute predicted glucose values ​​output by the ARIMA model within the time frame (e.g., 9AM values ​​to 10AM values), and the hourly predicted glucose values ​​output by the hourly forecast model (e.g., the predicted average glucose level within the 9AM to 10AM time window), resulting in a 5-minute ensemble forecast value consisting of 60% of the ARIMA predicted glucose values ​​for that particular 5-minute sampling time (e.g., the ARIMA predicted glucose value at 9:05AM) and 40% of the hourly predicted glucose values ​​within the forecast range. However, for the third hour before the current prediction time, the reliability of the patient's hourly forecast model may be fifty percent higher than the reliability of the predicted glucose value output by the ARIMA model within the third hour, resulting in a weighting factor assigned to the hourly forecast model that is fifty percent higher than the weighting factor assigned to the ARIMA model, thereby deriving a 5-minute ensemble forecast value consisting of 40% of the ARIMA predicted glucose values ​​at a specific 5-minute sampling time (e.g., the ARIMA predicted glucose values ​​for 10:05AM, 10:10AM, etc.) and 60% of the hourly forecast glucose values ​​within the time range (e.g., the hourly forecast glucose values ​​within the time period from 10AM to 11AM).

[0142] After determining the future aggregate glucose forecast, the aggregate prediction process 1100 proceeds to generate or otherwise provide a graphical representation of the aggregate predicted glucose value to the patient or other user (task 1114). Fig.12As depicted, a GUI display 1200 may be presented on the client electronic device 106 and / or infusion device 602 that includes a graphical representation 1202 of the patient's sensor glucose measurement data relative to a time before a marker 1206 or a similar graphical indication of the current time, followed by a graphical representation 1204 of the aggregate predicted glucose value relative to a time after a marker indicating the current time. In one or more embodiments, as the prediction horizon progresses further into the future before the current time, the reliability metric associated with the ARIMA model, physiological model, or other short-term forecast model decreases relative to the reliability metric associated with the hourly forecast model, such that when the patient or user scrolls, slides, or otherwise adjusts the GUI display 1200 to advance the prediction horizon associated with the displayed values, the graphical representation of the aggregate predicted glucose values ​​converges toward the hourly predicted glucose level. In this regard, scrolling or adjusting the sensor glucose measurement display area 904 may result in updating the sensor glucose measurement display area 904 to present the GUI display 1200 that depicts the aggregate glucose forecast 1204 extending from the current time marker 1206 into the future.

[0143] In one or more exemplary embodiments, the earlier portion of the ensemble glucose forecast 1204 is weighted more heavily toward the prediction model output that is more reliable in the short term, while the future portion of the ensemble glucose forecast 1204 will be weighted more heavily toward the prediction model output that has better long-term reliability. For example, the portion of the ensemble glucose forecast 1204 from 10AM to 11AM may be composed of 60% of the output of the patient's ARIMA model and 40% of the patient's hourly forecast model, while the subsequent portion of the ensemble glucose forecast 1204 from 11AM to 12PM may be composed of 40% of the output of the patient's ARIMA model and 60% of the patient's hourly forecast model, the portion of the ensemble glucose forecast 1204 from 12PM to 1PM may be composed of 30% of the output of the patient's ARIMA model and 70% of the patient's hourly forecast model, and so on.

[0144] Fig.13 An exemplary patient simulation process 1300 suitable for implementation in conjunction with the ensemble prediction process 1100 is depicted for simulating or otherwise predicting how different events or actions performed by a patient may affect the patient's glucose level in the future. The various tasks performed in conjunction with the patient simulation process 1300 may be performed by hardware, firmware, software executed by processing circuitry, or any combination thereof. For purposes of illustration, the following description relates to the above-described ensemble prediction process 1100. Figure 1 and Figure 6 to Figure 7mentioned elements. In practice, portions of the patient simulation process 1300 may be performed by different elements of the patient data management system 100 or the infusion system 600 (e.g., the server 102, one or more electronic devices 106, the infusion device 602, and / or the pump control system 620, 700). It should be understood that the patient simulation process 1300 may include any number of additional or alternative tasks, which need not be performed in the order shown and / or which may be performed simultaneously, and / or the patient simulation process 1300 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. Furthermore, the patient simulation process 1300 may be omitted from an actual implementation of the patient simulation process 1300 as long as the intended overall functionality remains unchanged. Fig.13 One or more tasks shown and described in the context of.

[0145] Patient simulation process 1300 begins with receiving or otherwise obtaining the user input (task 1302) of the future event, action or other activities of the indication patient. In this regard, the patient or another user can input or otherwise provide information characterizing, quantifying or otherwise defining the action or event that the patient expects, thinks or otherwise considers. For example, the user input can indicate the expected amount of carbohydrate that the patient will consume, the expected amount of exercise that the patient will perform, the expected amount of insulin that the patient will use, etc. In addition, the user input can indicate the time of the day associated with the expected activity (for example, the expected meal time for the carbohydrate of the future amount), the time window of interest or the duration, or other time information of the expected activity that the characterization patient carries out. In some events, prediction engine 708 can automatically predict future actions or events and the corresponding parameters or standards associated therewith based on the patient's historical measurement data, event log data, background data, etc. In such embodiments, the patient or another user can input or otherwise provide confirmation to predicting future events, or otherwise adjust one or more characteristics associated with the future events of prediction (for example, adjusting the future time, quantity, duration, type or other characteristics, etc., associated with the event).

[0146] The patient simulation process 1300 continues by calculating or otherwise determining adjusted weighting factors for combining the outputs from the patient's glucose prediction models into an ensemble prediction based on the current operating context and the input future activity information (task 1304). In this regard, similar to what was described above in the context of the ensemble prediction process 1100, the patient simulation process 1300 calculates or otherwise determines reliability metrics associated with different patient-specific glucose prediction models for different prediction horizons into the future, based on the current time of day and other operating contexts, in a manner that also takes into account the input future activity information.

[0147] In one or more embodiments, when selecting a subset of the patient history data 120 for determining a reliability metric, the patient simulation process 1300 may exclude from the subset of the patient history data 120 corresponding to the current operating context any subset of the patient history data 120 that corresponds to the current operating context that does not contain one or more inputted future activities within the prediction range or otherwise within a threshold time period from the current time of day. For example, if the current operating context indicates that it is 8 AM on Wednesday, and the patient is at home, and the user input indicates that the patient intends to consume carbohydrates or otherwise experience a meal event within a threshold amount of time, then only previous subsets of the patient history data 120 that have an associated timestamp at or around 8 AM on Wednesday and / or have an associated geographic location that corresponds to the patient's home geographic location that also have simultaneous or concurrent meals within the threshold amount of time after 8 AM are selected for analysis. In this regard, changing the data sets used to calculate the reliability metrics associated with different prediction models can produce forward-looking adjusted reliability metric values ​​associated with the respective prediction models that are different from normal reliability metric values ​​that would otherwise be associated with the respective prediction models without consideration of future activity. The patient simulation process 1300 then determines forward-looking adjusted weighting factors based on the relationship between the adjusted reliability metrics across the different patient-specific glucose prediction models. In a similar manner as described above, the forward-looking adjusted weighting factors can also vary relative to the prediction time before the current time.

[0148] Still reference Fig.13 , after determining the weighting factors that are prospectively adjusted to take into account the input future activity information, the patient simulation process 1300 uses the prospectively adjusted weighting factors to calculate or otherwise determine the expected set glucose forecast based on the input future activity information (task 1306). In this regard, the input future activity information is provided as input to one or more patient glucose prediction models so as to thereby change or affect the predicted glucose value output by the model in a manner that takes into account the prescribed future events at the corresponding time in the future. For example, if the user input indicates that the patient may eat at a specific time in the future, the input meal information will be provided as input to the LSTM cell of the patient's hourly forecast model, which contains or corresponds to the future time, thereby affecting the predicted glucose value of the time interval and / or subsequent time intervals. As another example, if the user input indicates that the patient may be administering a bolus of insulin at the current time, the input bolus amount may be provided to each glucose prediction model of the patient in a manner that takes into account the insulin bolus amount at initialization (e.g., by adding the input bolus amount to the current active insulin amount associated with the current time of the day).

[0149] After using each glucose prediction model of the patient to calculate the predicted glucose value that takes into account the expected patient activity, in a manner similar to that described above, the collective prediction value is determined as a weighted average of the corresponding predicted glucose values ​​output by the corresponding glucose prediction model using the prospectively adjusted weighting factors (e.g., task 1112). By prospectively adjusting the weighting factors and using future activities as inputs to the prediction models, if the patient engages in the input activities, the resulting collective prediction value will effectively simulate or project the patient's glucose level. Therefore, the expected collective prediction can be alternatively referred to as the patient's simulated glucose level in this article.

[0150] In an exemplary embodiment, the patient simulation process 1300 generates or otherwise provides a graphical representation of the patient's simulated glucose level or other feedback affected by the expected set glucose prediction (task 1308). For example, in some embodiments, a line graph or a curve graph of the patient's simulated glucose level can be presented in response to the input future activity information. In other embodiments, the simulated glucose values ​​can be processed or otherwise analyzed to provide one or more recommendations to the patient (e.g., an indication of whether to participate in the input activity, etc.).

[0151] For example, now refer to Fig.14 ,refer to Fig.13In one or more embodiments, the patient simulation process 1300 is performed in conjunction with a session interaction with the patient that may be supported by a session interaction application 712 at an infusion device 602 or other client device 106. In the illustrated embodiment, the patient manipulates or otherwise interacts with the client device 106, 602 to input that the patient wishes to view his simulated glucose level over the next 4 hours if the patient also consumes 60 grams of carbohydrates and administers a bolus of 3 units of insulin. In response to receiving the user input, the session interaction application 712 may provide the input parameters to the prediction engine 708 for simulating the patient's glucose level according to the patient simulation process 1300. In this regard, for the subsequent 4 hours after the patient's history of consuming carbohydrates and / or administering an insulin bolus at or around the current time of day, the patient simulation process 1300 determines a forward-looking adjusted weighting factor for the patient's prediction model based on the corresponding reliability metric associated with the model. The patient simulation process 1300 then inputs or otherwise provides 60 grams of carbohydrates and 3 units of insulin to each patient's prediction model to initialize the model as if carbohydrates were being consumed and insulin was being delivered simultaneously or otherwise when the model was started. Thereafter, the patient's prediction model is used to calculate the patient's predicted glucose value for the next 4 hours, which has been initialized with 60 grams of carbohydrates and 3 units of insulin. The expected set predicted glucose value for the next 4 hours is then determined as a weighted average of the predicted glucose values ​​using the forward-looking adjusted weighting factor, which takes into account the simultaneous intake of carbohydrates and insulin.

[0152] After determining the expected set predicted glucose value for the patient, the prediction engine 708 can provide the expected set predicted glucose value to the conversation interaction application 712 for presentation to the patient in the context of the ongoing conversation interaction. In this regard, the conversation GUI display 1400 including the graphical representation 1402 of the user input is updated to include a conversation response 1404 of the user input affected by the simulated glucose value. In the illustrated embodiment, the conversation response 1404 includes a graphical representation 1406 (e.g., a line graph of the expected set predicted glucose value) of the patient's simulated glucose level over the next 4 hours following a marker 1408 indicating the current time of day.

[0153] Fig.15 Another exemplary GUI display 1500 is depicted, which depicts a mergeable Fig.11 The ensemble prediction process 1100 and / or Fig.131300. In the illustrated embodiment, the conversation interaction application 712 receives initial user input 1502 and analyzes the initial user input to determine that the patient is interested in the prediction of his or her physiological condition. The conversation interaction application 712 generates or otherwise provides a conversation response 1504 that prompts the patient to enter or otherwise provide possible other parameters for the prediction range and / or the prediction to be performed. For example, in some embodiments, the conversation interaction application 712 can prompt the patient to provide input for any expected activities within the prediction range for prospectively adjusting the prediction according to the patient simulation process 1300.

[0154] In response to receiving subsequent user input 1506 indicating that the patient is interested in the prediction within the next 12 hours, the conversational interaction application 712 commands, signals, or otherwise instructs the prediction engine 708 to predict the patient's glucose level within the next 12 hours. The prediction engine 708 performs the collective prediction process 1100 to calculate or otherwise determine the patient's collective predicted glucose value within the next 12 hours based on the patient's current or most recent glucose measurement, current active insulin, current operating context, etc. as described above. In the illustrated embodiment, the prediction engine 708 also utilizes a reliability metric (e.g., standard deviation, mean absolute difference, etc.) associated with the relevant prediction model to probabilistically determine the likelihood of one or more physiological events (e.g., hypoglycemic events, hyperglycemic events, and / or similar events) within the prediction range based on the collective predicted glucose values. The prediction engine 708 provides the collective predicted glucose values ​​and corresponding physiological event probabilities to the conversational interaction application 712, which generates a conversational response 1508 that provides feedback affected by the patient's collective predicted values. For example, the illustrated session response 1508 includes a graphical representation of the probability of a hypoglycemic event relative to different intervals within the forecast range, as well as an indication of the probability of a hypoglycemic event based on the current time of day.

[0155] In the illustrated embodiment, the conversation response 1508 also prompts the patient whether the patient wishes to configure one or more settings at the device 106, 602 based on the predicted glucose level. In response to receiving the user input 1510 indicating a desire to configure a reminder, the conversation interaction application 712 can configure itself to provide a reminder at a time of day when the probability of a hypoglycemic event is highest based on the aggregate prediction value and the reliability metric, and then generate or otherwise provide a conversation response 1512 confirming or otherwise indicating that the reminder has been set.

[0156] Fig.16Another exemplary GUI display 1600 is depicted that depicts a conversational interaction that may incorporate one or more of the above processes 300, 800, 1100, 1300. In the illustrated embodiment, the conversational interaction application 712 receives an initial conversational user input 1602 and analyzes the initial user input to determine that the patient is interested in a prediction of his or her physiological condition in response to a future exercise event. The conversational interaction application 712 generates or otherwise provides a series of conversational responses 1604, 1608, 1612 that prompt the patient to conversationally input 1606, 1610, 1614 and define the expected attributes of the future exercise event, such as the expected type of event, the expected duration of the event, and the expected timing of the event. After defining the attributes of the future event, the prediction engine 708 performs a patient simulation process 1300 to calculate or otherwise determine the patient's expected glucose level after the event (e.g., at a time corresponding to the sum of the input timing 1614 of the event and the input duration 1610 of the event) based on the patient's current glucose measurement, current active insulin, and current operating background, where the attributes associated with the future event are input or otherwise provided to the patient's forecast and prediction model based on the expected time entered by the patient.

[0157] In some embodiments, in order to adjust the model weighting factors, reliability metrics, or other aspects of the patient simulation process 1300 to take into account the expected patient activities, the query process 300 can be performed to obtain data or information characterizing the responses of other similar patients to the expected future events. For example, the query process 300 can be performed to identify similar patients based on the common links or edges between the nodes within the logical database layer, and obtain historical measurement data of the blood glucose response of these similar patients to the input activity type for the input duration at the input time of the day (e.g., sensor glucose measurement data when a similar patient jogs at 10AM and continues for the next 30 minutes). Then, the average or typical physiological response of the similar patients can be used to adjust or otherwise enhance the physiological prediction model of the individual patient, which is then used by the ensemble prediction process 1100 and / or the patient simulation process 1300 to obtain the expected ensemble glucose prediction, which is affected by the patient's hourly glucose forecasts, which are taken into account for the input future exercise (e.g., by inputting exercise attributes into the 10AM LSTM unit) and combined with the adjusted physiological prediction for the patient's glucose level.

[0158] As another example, a patient may interact with a client device 106, 602 conversationally to obtain a prediction of what his or her sensor glucose level may be when waking up in the morning. Based on the patient's historical event log data, the patient's estimated sleep time and / or estimated wake-up time may be determined, and it may be used to adjust the model weighting factor and provided as an input to the patient prediction model to obtain an expected set of predictions for the patient's glucose level at or around the estimated wake-up time. In one or more embodiments, the expected set glucose prediction is also used to generate one or more suggestions for the patient. For example, if the expected set glucose prediction performed at the estimated wake-up time exceeds the range of the target glucose value, the query process 300 may be performed to identify actions performed by similar patients at or before bedtime or otherwise associated with overnight periods that cause these patients' glucose levels to change, which, if a corresponding increase or decrease occurs relative to the current patient, will result in the expected set glucose prediction at the estimated wake-up time being within the target range. In this regard, the query process 300 may be used to identify the recommended amount of carbohydrates that the patient should consume, the recommended amount of insulin that the patient should bolus, and / or the recommended amount of exercise that the patient should perform before going to bed, so as to achieve the desired glucose level when waking up. If the expected collective glucose prediction at the estimated wake time is within the range of target glucose values, other suggestions that may improve the patient's glucose regulation (e.g., increasing the percentage of the day that is within the target glucose range, minimizing glucose excursion events, etc.) can be determined based on similar patients and provided to the patient, for example, a recommended duration of sleep, a recommended amount of carbohydrates for the next day, a recommended amount of exercise for the next day, etc.

[0159] refer to Figures 8 to 16 And refer to Figure 1 In some embodiments, one or more of the processes 800, 1100, 1300 may be implemented in conjunction with the patient data management system 100 and adapted to utilize the graph data structures in the database 104 to improve the accuracy of modeling and outcome prediction. In this regard, weighted directed or causal links between nodes or entities may be used to identify predictive relationships and corresponding impacts on patient outcomes for improved modeling, while such relationships may otherwise be impossible or computationally impractical to determine using conventional databases that rely on tables lacking causal and / or probabilistic relationships between entities.

[0160] Anticipatory management

[0161] Reference now Fig.17According to one or more embodiments, the risk management process 1700 utilizes measurement data related to the patient's physiological condition in combination with the patient's medical record data in order to calculate or otherwise determine a metric indicating the patient's risk of experiencing a particular condition. For example, the patient's sensor glucose measurement data or a metric calculated based on that data may be utilized in conjunction with a subset of the patient's medical record data in order to calculate or otherwise determine a metric indicating the patient's risk of experiencing one or more acute diabetic crises (e.g., severe hypoglycemia, acute diabetic ketoacidosis, hyperosmolarity, etc.) and / or long-term complications. In an exemplary embodiment, the risk management process 1700 generates or otherwise provides a notification or recommendation to an end user (e.g., a patient, a patient's healthcare provider, a patient's care partner, etc.) regarding a condition in which the patient is at risk. For purposes of illustration, the subject matter may be described herein in the context of providing notifications or recommendations to a patient, however, it should be understood that the subject matter described herein is not limited to the type of end user to whom the notifications or recommendations are provided. In some embodiments, according to one or more of processes 1800 and / or processes 1900, one or more treatment recommendations are provided to the patient, as described below in Figure 18 to Figure 19 In addition, in Fig.17 In the illustrated embodiment, the value of a metric indicative of a patient's risk level for a particular condition can be used to adjust, modify, or otherwise affect the delivery of fluid by an infusion device 602 associated with the patient and / or otherwise alter the patient's treatment.

[0162] The various tasks performed in conjunction with the risk management process 1700 may be performed by hardware, firmware, software executed by a processing circuit, or any combination thereof. Figure 1 and Figure 6 to Figure 7 elements mentioned. In practice, portions of the risk management process 1700 may be performed by different elements of the patient data management system 100 or the infusion system 600 (e.g., the server 102, one or more electronic devices 106, the infusion device 602, and / or the pump control system 620, 700). It should be understood that the risk management process 1700 may include any number of additional or alternative tasks, which need not be performed in the order shown and / or which may be performed simultaneously, and / or the risk management process 1700 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. Furthermore, the risk management process 1700 may be omitted from an actual implementation of the risk management process 1700 as long as the intended overall functionality remains unchanged. Fig.17 One or more tasks shown and described in the context of.

[0163] In the illustrated embodiment, the risk management process 1700 begins by receiving or otherwise obtaining measurement data and medical record data for a patient population (tasks 1702, 1704). For example, the server 102 may retrieve a subset of the historical patient data 120 for the patient population and a corresponding subset of the electronic medical record data 122 for the patient population from the database 104. In one embodiment, the historical patient data 120 and the electronic medical record data 122 are obtained for all common patients across the data sets. However, in other embodiments, the patient population may be tailored for a particular demographic or combination of demographic attributes (e.g., by age, gender, income, etc.).

[0164] After obtaining the measurement data and medical record data for the patient population, the risk management process 1700 determines a risk model for a particular condition based on the relationship between the measurement data and the medical record data across the patient population (task 1706). In an exemplary embodiment, stepwise feature selection, such as recursive feature elimination, is performed to identify which fields or attributes of the patient measurement data and medical record data are most correlated with or predictive of the occurrence of a particular condition within the patient population.

[0165] For example, the server 102 may analyze historical sensor glucose measurement data for a patient population to identify which sensor glucose metrics (e.g., average sensor glucose measurement value, sensor glucose measurement standard deviation, overnight average sensor glucose measurement value, percentage of time that sensor glucose measurement value is within range, percentage of time that sensor glucose measurement value is above a hyperglycemic threshold, percentage of time that sensor glucose measurement value is below a hypoglycemic threshold, etc.) for a subset of the patient population predict or correlate with the occurrence of a particular medical diagnosis code within the electronic medical records of the subset of the patient population. In this regard, for a given medical diagnosis code of interest (e.g., hypoglycemia, diabetic ketoacidosis, hyperosmolarity, cardiovascular disease, etc.), the server 102 may perform stepwise feature selection across different sensor glucose measurement metrics associated with the patient population to identify or determine a subset of sensor glucose measurement metrics that correlate with or predict the occurrence of a diagnosis code for that medical condition within the electronic medical record data 122. Similarly, for a medical condition of interest, the server 102 may analyze the electronic medical record data of the patient population by performing stepwise feature selection to identify which fields or attributes of the patient medical records (e.g., age, gender, income, education, smoking, A1C value or other laboratory value, insulin status or other medication or therapy, other medical conditions, etc.) are associated with or predict the occurrence of the disease condition. It should be noted that in some embodiments, the operational context data of the patient population may also be analyzed to identify whether any particular operational environment (e.g., geographic location, temperature, humidity, and / or similar conditions) is associated with or predicts the occurrence of a particular medical condition.

[0166] After identifying the sensor glucose measurement variables and medical record variables that are related to or predict the occurrence of the medical condition, the server 102 calculates or otherwise determines an equation, function or model for calculating the probability or possibility of the occurrence of the medical condition of interest based on the predicted subset of the sensor glucose measurement variables and the medical record variables. For example, the risk prediction model of cardiovascular disease can calculate the probability of the patient suffering from cardiovascular disease in the future based on the following: the average sensor glucose measurement value of the patient, the sensor glucose measurement standard deviation, the percentage of time that the patient's sensor glucose measurement value exceeds the target range, the patient's age, and whether the patient receives insulin treatment. Depending on the embodiment, the risk prediction model can calculate the risk probability within a limited future prediction range (e.g., within the next 18 months, within the patient's life expectancy, etc.) or within an infinite or unbounded duration. After determining the risk prediction model for various medical conditions and / or patient populations, the server 102 can store or maintain the risk prediction model for different medical conditions in a database 104 associated with the overall statistical standard of the patient population for the corresponding model. In other embodiments, the server 102 can transmit or push the risk prediction model to one or more client electronic devices 106, 602. In this regard, in some embodiments, the server 102 may periodically update the risk prediction model (e.g., weekly, monthly, yearly, etc.) to reflect new or more recent data in the database 104.

[0167] Still reference Fig.17 , the risk management process 1700 shown receives or otherwise obtains the measurement data and medical record data of an individual patient, and applies one or more risk prediction models to the measurement data and medical record data of the patient to determine the individual risk of the patient experiencing a condition associated with the corresponding risk prediction model (tasks 1708, 1710, 1712). In this regard, the risk prediction model can be used to periodically or continuously analyze the sensor glucose measurement data and electronic medical record data of the individual patient to determine whether the patient has a risk of a specific condition above a threshold risk tolerance. In one or more exemplary embodiments, the risk of an individual patient for a specific condition is analyzed or otherwise determined at a client device 106, 602 associated with the patient. In this regard, the client device 106, 602 can download or otherwise retrieve a risk prediction model for a condition that has not or has not yet been diagnosed for its associated patient from the database 104 via the server 102. The overall statistical information and medical record data associated with the patient can be used to identify the patient group to which the patient belongs, and then for the medical condition without a diagnosis code present in the patient's medical record data, a risk prediction model associated with the identifier patient group is selected.

[0168] After obtaining the risk prediction model for a specific medical condition, the client device 106, 602 uses the current or most recent sensor glucose measurement data associated with the patient to calculate or otherwise determine one or more inputs to the risk prediction model. In addition, the client device 106, 602 can download or otherwise retrieve fields of the patient's medical record data from the database 104 via the server 102, which are also used as inputs to the risk prediction model. Then, the client device 106, 602 uses equations, formulas, or functions associated with the risk prediction model to calculate or otherwise determine an output value representing the probability of the patient developing or experiencing a medical condition associated with the risk prediction model based on the patient's most recent measurement data and medical record fields, i.e., the patient's risk score for the condition. In addition, in an embodiment in which the risk prediction model uses background information as input, the client device 106, 602 can obtain the current operating context via one or more sensing devices 650, 660 at the client device 106, 602, and input the current operating context into the risk prediction model.

[0169] In an exemplary embodiment, when the patient's risk score is greater than the notification threshold, the risk management process 1700 generates or otherwise provides a user notification indicating a potential risk to the patient (tasks 1714, 1716). For example, a user notification may be generated or otherwise provided at a client device 106, 602, which identifies a medical condition in which the patient may be experiencing or exhibiting a risk. In some embodiments, the risk management process 1700 generates or otherwise provides treatment recommendations based on the medical condition. In this regard, the patient measurement data and / or medical record data input to the risk prediction model may be analyzed to identify or otherwise determine whether any input variables can be modified to reduce the patient's risk score and provide the patient with recommended remedial measures. For example, a GUI display may be generated at a client device 106, 602, which includes recommended actions (e.g., exercise, dietary changes, etc.) that the patient can take to reduce his average sensor glucose measurement when a higher sensor glucose measurement predicts a specific medical condition. As another example, the GUI display at the client device 106, 602 may include recommended treatment changes (e.g., changing the type of treatment, adding a new medication, etc.). In this regard, the risk management process 1700 may initiate the process 1800 described below to identify which therapy modifications should be recommended to the patient to achieve a desired reduction in the patient's risk score.

[0170] Still reference Fig.17In one or more exemplary embodiments, the risk management process 1700 adjusts or modifies the delivery of fluid by the infusion device based at least in part on the patient's risk score for a particular medical condition (task 1718). In this regard, based on the patient's risk score and / or the magnitude of the medical condition, the command generation application 710 can adjust one or more delivery commands to compensate for the patient's risk. For example, when the patient's risk score indicates that the patient's risk of a severe hypoglycemic event is greater than a threshold probability, the command generation application 710 can reduce the delivery command to mitigate the risk of a hypoglycemic event. Therefore, even if the patient's current sensor glucose measurement or a glucose level predicted based on a previous measurement or trend is above the hypoglycemic threshold, or is otherwise expected to remain within a target glucose value range, the command generation application 710 can reduce insulin delivery (e.g., by narrowing or reducing the delivery command, increasing the patient's target glucose level, increasing the patient's insulin sensitivity factor, etc.) to proactively account for the increased risk of hypoglycemia. As another example, when the patient's risk score indicates that the patient's risk of diabetic ketoacidosis or other acute hyperglycemic events is greater than a threshold probability, the command generation application 710 can increase delivery commands, reduce the patient's insulin sensitivity factor, and / or reduce the patient's target glucose value to mitigate the risk by increasing insulin in the patient's body. Thus, even if the patient's current sensor glucose measurement or predicted glucose level is below the hyperglycemic threshold, or is otherwise expected to remain within the target glucose value range, the command generation application 710 can increase insulin delivery to proactively reduce the patient's risk of hyperglycemic events. In some embodiments, the risk management process 1700 can dynamically determine the risk score in real time in response to new or updated sensor glucose measurements, and stop modifying the delivery command once the risk of a patient's particular condition drops below a threshold.

[0171] Reference now Fig.18In one or more exemplary embodiments, a boost recommendation process 1800 is performed to identify a treatment recommendation that may have the most favorable impact on the patient's physiological condition based on the patient's historical data (e.g., measurement data, event log data, background data, etc.) and medical record data. In some embodiments, the boost recommendation process 1800 can identify which therapy change or intervention may have the greatest impact on aspects of the individual's physiological condition. In other embodiments, a boost metric is used in conjunction with cost, compliance, patient burden, and / or other metrics to apply a cost-effectiveness analysis or similar optimization technique to identify the best treatment recommendation for the patient. It should be noted that although the terms boost, boost modeling, and variations thereof may be used for purposes of explanation, the present subject matter is not limited to boost modeling. Therefore, in the absence of explicit indication, boost modeling should be understood to include any type of incremental modeling of the impact of a particular event or action on a particular outcome, including true referral modeling, net referral modeling, and variations thereof.

[0172] The various tasks performed in conjunction with the improvement suggestion process 1800 may be performed by hardware, firmware, software executed by a processing circuit, or any combination thereof. Figure 1 and Figure 6 to Figure 7 elements mentioned. In practice, portions of the escalation suggestion process 1800 may be performed by different elements of the patient data management system 100 or the infusion system 600 (e.g., the server 102, one or more electronic devices 106, the infusion device 602, and / or the pump control system 620, 700). It should be understood that the escalation suggestion process 1800 may include any number of additional or alternative tasks, which need not be performed in the order shown, and / or which may be performed simultaneously, and / or the escalation suggestion process 1800 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. Furthermore, as long as the intended overall functionality remains unchanged, the escalation suggestion process 1800 may be performed in a manner that is consistent with the present disclosure. Fig.18 One or more of the tasks shown and described in the context of may be omitted from an actual implementation of the promotion suggestion process 1800.

[0173] The promotion suggestion process 1800 receives or otherwise obtains historical patient data and medical record data for a patient population, and then analyzes the relationships between the historical patient data and the medical record data to identify different patient groups for modeling the effects of different therapeutic interventions on the patient's physiological condition (tasks 1802, 1804, 1806). For example, the server 102 can retrieve the historical patient data 120 and the electronic medical record data 122 from the database 104, and then utilize machine learning to identify patient groups where different therapeutic interventions or changes have statistically significant improvements in physiological patient aspects within the patient group, such as, for example, a reduction in A1C lab values, a reduction in glucose excursion events, an increase in the percentage of time that sensor glucose measurements are within a target range, etc. In this regard, patient groups can be defined by common overall demographic attributes (e.g., gender, income, etc.), common medical diagnoses, common treatment regimens or types of treatments (e.g., monotherapy patients, dual therapy patients, etc.), common medications or prescriptions, and / or other medical record commonalities. For example, in addition to defining patient groups statistically in general (e.g., by age, location, race, gender, socioeconomic status, occupation, etc.), clustering techniques can be used to characterize or define patient groups to categorize similar patients using other available data sets (e.g., emotional records, program interactions, personal goals, etc.), which can be tracked, monitored, or recorded by an application at the client device 106, for example.

[0174] After identifying different patient groups to model for different therapeutic interventions, the lift recommendation process 1800 determines a lift model for calculating the impact of the corresponding therapeutic intervention on each patient group (task 1808). In this regard, the server 102 identifies sensor glucose measurement variables, medical record variables, and / or operating context variables that are associated with or predict an improvement in the physiological condition, and then calculates or otherwise determines an equation, function, or model for calculating a possible improvement in the physiological condition based on the identified subset of variables. For example, a stepwise feature selection may be performed to identify which fields or attributes of the patient measurement data and medical record data are most associated with or predict the amount of A1C reduction in the patient group. A lift model for calculating an estimated A1C reduction for patients within the particular patient group may then be determined based on the relevant subset of the sensor glucose measurement variables, medical record variables, and / or operating context variables. In this regard, for each different patient cohort identified for a different potential therapeutic intervention, the server 102 may determine a boost model for calculating a metric indicating the effect of the corresponding therapeutic intervention on the physiological condition of the corresponding cohort of patients based on a subset of sensor glucose measurement variables, medical record variables, and / or operating environment variables. The boost model determined by the server 102 may be stored or otherwise maintained in a database 104 associated with a corresponding combination of patient cohort attributes and therapeutic interventions. In some embodiments, the server 102 may push or otherwise transmit the boost model to a client device 106, 602 associated with a patient classified within the corresponding patient cohort. It should be noted that boost modeling is not limited to stepwise feature selection, and in other embodiments, random forest analysis, logistic regression, and / or other machine learning or artificial intelligence techniques may be utilized to generate the boost model.

[0175] Still reference Fig.18To determine treatment recommendations for individual patients, the lift recommendation process 1800 receives or otherwise obtains historical observed patient data and medical record data for the individual patient, and then identifies or otherwise obtains lift models associated with patient groups that include the patient or classify the patient into these groups based on the patient's overall demographic information, medical records, etc. (tasks 1810, 1812, 1814). In other words, the patient's medical records, measurement data, event log data, and / or current operating environment can be used to identify which lift models in the database 104 may be most relevant to the individual patient being analyzed. Thereafter, the lift recommendation process 1800 calculates or otherwise determines an impact or lift metric associated with each respective therapeutic intervention for the patient based on the patient's measurement data and medical record data and the respective lift models associated with the different therapeutic interventions (task 1816). In this regard, for each potential therapeutic intervention, the lift recommendation process 1800 may calculate or otherwise determine an estimated A1C reduction or other estimate of lift or impact associated with the corresponding therapeutic intervention for the patient based on the patient's medical records, measurement data, and / or current operating environment.

[0176] After determining the lift metrics for different possible therapeutic interventions for the patient, the lift suggestion process 1800 determines a therapeutic intervention recommendation based on the lift metrics, and generates or otherwise provides an indication of the recommended therapeutic intervention to the patient (tasks 1818, 1820). For example, in one embodiment, the lift suggestion process 1800 identifies the therapeutic intervention with the greatest estimated impact or benefit (e.g., the greatest estimated A1C reduction) as a recommended therapeutic intervention for the patient. In other embodiments, the lift suggestion process 1800 performs a cost-effectiveness analysis or other optimization to identify the best therapeutic intervention based on the estimated lift values ​​associated with different potential therapeutic interventions and the costs associated with the corresponding possible therapeutic interventions. For example, the lift suggestion process 1800 identifies the therapeutic intervention with the highest ratio of estimated lift value to cost as a recommended therapeutic intervention. In this regard, in some embodiments, the claims data 124 maintained in the database 104 can be used to calculate or otherwise determine an estimated cost associated with a particular therapeutic intervention, which in turn can be used to determine the relative impact or benefit of the therapeutic intervention (e.g., by dividing the lift value by the estimated cost).

[0177] In one or more embodiments, the escalation suggestion process 1800 identifies or otherwise determines the best therapeutic intervention based on: estimated escalation values ​​associated with different potential therapeutic interventions, costs associated with the different potential therapeutic interventions, and estimated adherence metrics associated with the different potential therapeutic interventions. In this regard, some embodiments of the escalation suggestion process 1800 may calculate or otherwise determine an adherence metric that represents the likelihood that a patient will adhere to a particular therapeutic intervention, as described below in Fig.19 1900. Thus, in some embodiments, the uplift suggestion process 1800 can identify the following for recommendation as the best treatment intervention: a treatment intervention that does not have the highest estimated uplift value, but has a relatively lower cost and / or higher compliance than one or more treatment interventions with higher estimated uplift values. Thus, the patient can be informed of the most cost-effective and more likely to be successful treatment based on the likelihood that the patient will comply with the recommended treatment.

[0178] Reference now Fig.19 In one or more exemplary embodiments, the adherence suggestion process 1900 may be performed to identify treatment recommendations that an individual patient is most likely to adhere to or will otherwise produce the highest adherence. In this regard, in the exemplary embodiments described herein, adherence modeling is used to determine an adherence metric that represents a respective probability that a patient will adhere to a particular treatment regimen, e.g., by taking a fully prescribed treatment regimen or engaging in some other action prescribed by the respective treatment regimen or indicating an attempt to achieve the respective treatment regimen (e.g., fulfilling a prescription within a threshold amount of time after being written). Using the adherence metric, a therapeutic intervention that may result in the patient having better adherence (and thereby being more likely to have a beneficial outcome relative to a treatment regimen that prescribes a level of adherence that is less likely) may be suggested to the patient. For example, for a given patient, if the adherence metric value associated with an injectable insulin treatment regimen (e.g., 15%) is lower relative to the adherence metric for an oral medication (e.g., 50%), then the oral medication is recommended because it may provide a greater improvement given its associated adherence.

[0179] The various tasks performed in conjunction with the compliance recommendation process 1900 may be performed by hardware, firmware, software executed by a processing circuit, or any combination thereof. Figure 1 and Figure 6 to Figure 7elements mentioned. In practice, portions of the compliance recommendation process 1900 may be performed by different elements of the patient data management system 100 or the infusion system 600 (e.g., the server 102, one or more electronic devices 106, the infusion device 602, and / or the pump control system 620, 700). It should be understood that the compliance recommendation process 1900 may include any number of additional or alternative tasks, which need not be performed in the order shown, and / or which may be performed simultaneously, and / or the compliance recommendation process 1900 may be incorporated into a more comprehensive program or process having additional functionality not described in detail herein. Furthermore, as long as the intended overall functionality remains unchanged, the compliance recommendation process 1900 may be performed in a manner that is consistent with the present disclosure. Fig.19 One or more of the tasks shown and described in the context of may be omitted from an actual implementation of compliance suggestion process 1900.

[0180] The compliance suggestion process 1900 receives or otherwise obtains historical observation data, medical record data, and medical claims data for a patient population from a database (tasks 1902, 1904, 1906). The compliance suggestion process 1900 calculates or otherwise determines compliance metrics for different treatment interventions or regimens based on the relationship between the historical observation data, medical record data, and medical claims data for the patient population (task 1908). For example, for each patient whose corresponding medical record and medical claims data are stored in the database 104, the server 102 may analyze the relationship between the patient's prescriptions and other treatment information from the patient's medical record data and the number and / or frequency of the patient's medical claims corresponding to those prescriptions or therapies in the patient's claims data, so as to determine the compliance metric associated with the patient for the corresponding treatment based on the patient's claims data for the degree of compliance or alignment with the patient's prescribed treatment. In this regard, a patient having claim data indicating that the prescription is fulfilled with a prescribed frequency or a relatively small delay after the prescription is written may be assigned a relatively high compliance value, while a patient whose claim data indicates that the prescription is not fulfilled regularly or in a timely manner may be assigned a relatively low compliance value. Additionally, in some embodiments, event log data or other observed patient data may also be utilized when determining compliance metrics. For example, a patient's event log data may indicate when a patient took a prescribed medication and the corresponding dosage, which may then be compared to prescription information from the patient's medical record data to determine the patient's behavior's compliance with the patient's prescribed treatment.

[0181] In the illustrated embodiment, after determining the adherence metrics associated with different therapies for different patients, the adherence suggestion process 1900 proceeds to analyze the relationships between the observed patient data, medical records, claims data, and adherence values ​​to determine adherence models for calculating adherence metrics for the different therapies based on the observed data, medical record data, and claims data for the individual patients (task 1910). In this regard, for a subset of patients who share a particular treatment regimen, the server 102 identifies observed patient variables (e.g., sensor glucose measurement variables, meal, exercise or other event log variables, operating environment variables, etc.), medical record variables that are associated with or predictive of patient adherence metric values ​​for the treatment regimen (e.g., population demographic information, medical conditions, and / or claims data variables (e.g., refill data for previous prescriptions, etc.), and then calculates or otherwise determines an equation, function, or model for calculating a possible adherence metric value for a given patient based on the identified subset of variables associated with the prospective patient. For example, stepwise feature selection or other machine learning techniques may be performed to identify which fields or attributes of the historically observed patient data 120 and the medical record data 122 are most correlated with or predictive of the adherence metric value in the patient prescribed by the corresponding prescribed treatment regimen. A compliance model for calculating the estimated adherence of patients who are not currently taking the treatment regimen may then be determined based on the relevant subset of variables. In this regard, for each different potential treatment regimen or intervention, the server 102 may determine an adherence model for calculating a metric indicating possible adherence to the corresponding treatment regimen based on existing patients who are not prescribed the corresponding treatment regimen. The adherence model determined by the server 102 may be stored or otherwise maintained in a database 104 associated with the corresponding treatment regimen or intervention, or pushed or otherwise transmitted to the client device 106, 602.

[0182] Still reference Fig.19 To determine a treatment recommendation for an individual patient, the adherence recommendation process 1900 receives or otherwise obtains observational data, medical record data, and claims data for the individual patient, and then applies various adherence models for different treatment regimens that are not currently prescribed to the patient in order to calculate or otherwise determine adherence metrics for the different treatment regimens (tasks 1912, 1914). In this regard, the server 102 or client device 106, 602 obtains data associated with the patient of interest from the database 104, as well as recent measurement data and / or operational context information from the client device 106, 602 associated with the patient, and then utilizes the adherence model to estimate the patient's likely adherence for each different possible treatment regimen that is not currently prescribed for the patient.

[0183] In an exemplary embodiment, the adherence suggestion process 1900 determines a treatment recommendation for the patient based on the adherence metric values ​​associated with different possible treatment regimens, and generates or otherwise provides an indication of the treatment recommendation to the patient or another user (e.g., a physician, a healthcare provider, etc.) (tasks 1916, 1918). In some embodiments, the adherence suggestion process 1900 selects or otherwise identifies the treatment regimen with the highest adherence metric value as the recommended treatment for the patient. In other embodiments, the adherence metric values ​​associated with the different possible treatment regimens are considered in conjunction with the lift metric values ​​and / or estimated costs associated with the different possible treatment regimens to identify the best treatment regimen, as described above in Fig.19 . For example, in an embodiment where the compliance metric represents a probability or percentage, the lift metric associated with a possible treatment regimen for a patient of interest may be scaled or otherwise multiplied by the compliance metric associated with the treatment regimen for the patient of interest to obtain a possible lift value for the patient representing the possible benefit after taking into account compliance. In one embodiment, the recommended therapy may be selected as the treatment regimen with the highest possible lift value to cost ratio (e.g., the product of the lift metric and the probability of compliance divided by the estimated cost). A GUI display may be generated or otherwise provided at a client device 106, 602 that indicates the recommended treatment to the patient or other user of the client device 106, 602. In one embodiment, the GUI display may include a list of possible therapies that are categorized, prioritized, or otherwise sorted in a manner affected by the compliance metric so that the highest priority therapy corresponds to the treatment regimen recommended based on the compliance metric.

[0184] refer to Figures 17 to 19 It should be noted that in some embodiments, processes 1700, 1800, 1900 may be combined Figure 1 The patient data management system 100 is implemented in accordance with the present invention and is adapted to utilize the graph data structure in the database 104 to improve the accuracy of modeling. In this regard, weighted directed or causal links between nodes or entities can be used to identify predictive relationships and corresponding impacts on patient outcomes for improved modeling. Additionally, shared links within or between logical database layers can be utilized to identify commonalities between patients that may not otherwise be easily identified using conventional databases that rely on tables that lack causal and / or probabilistic relationships between entities.

[0185] Infusion system integration

[0186] Fig. 20An exemplary embodiment of an infusion system 2000 suitable for use with the above-described subject matter is depicted. For example, a computer 2008 (e.g., computing device 102) can communicate with and / or obtain data from various client electronic devices (e.g., electronic device 106), such as a fluid infusion device (or infusion pump) 2002 (e.g., infusion device 602), a sensing device 2004 (e.g., glucose sensing device 604), and a command control device (CCD) 2006. The components of the infusion system 2000 may be implemented using different platforms, designs, and configurations, and Fig. 20 The embodiments shown are not intended to be exhaustive or limiting. In practice, the infusion device 2002 and the sensing device 2004 are fixed to a desired position on the user's (or patient's) body, such as Fig. 20 In this regard, the infusion device 2002 and the sensing device 2004 are Fig. 20 The locations where the infusion system 2000 is secured to the user's body are provided only as representative, non-limiting examples. The elements of the infusion system 2000 may be similar to those described in U.S. Patent No. 8,674,288, the subject matter of which is hereby incorporated by reference in its entirety.

[0187] exist Fig. 20 In the exemplary embodiment of the infusion device 2002, the infusion device 2002 is designed as a portable medical device suitable for infusing a fluid, liquid, gel or other medicament into the body of a user. In the exemplary embodiment, the infused fluid is insulin, but many other fluids can be administered by infusion, such as but not limited to HIV drugs, drugs for treating pulmonary hypertension, iron chelation drugs, pain medications, anti-cancer treatment drugs, vitamins, hormones, etc. In some embodiments, the fluid may include nutritional supplements, dyes, tracking media, saline media, hydration media, etc.

[0188] The sensing device 2004 generally represents a component of the infusion system 2000 that is configured to sense, detect, measure, or otherwise quantify a condition of the user, and may include sensors, monitors, etc. for providing data indicative of the condition sensed, detected, measured, or otherwise monitored by the sensing device. In this regard, the sensing device 2004 may include electronics and enzymes that react to a biological condition of the user, such as blood glucose level, and provide data indicative of the blood glucose level to the infusion device 2002, CCD 2006, and / or computer 2008. For example, the infusion device 2002, CCD 2006, and / or computer 2008 may include a display for presenting information or data to the user based on the sensor data received from the sensing device 2004, such as, for example, the user's current glucose level, a graph or chart of the user's glucose level relative to time, a device status indicator, an alarm message, etc. In other embodiments, the infusion device 2002, CCD 2006, and / or computer 2008 may include electronics and software configured to analyze sensor data and operate the infusion device 2002 to deliver fluid to the user's body based on the sensor data and / or a preprogrammed delivery plan. Thus, in an exemplary embodiment, one or more of the infusion device 2002, the sensing device 2004, the CCD 2006, and / or the computer 2008 include a transmitter, a receiver, and / or other transceiver electronics that allow communication with other components of the infusion system 2000, such that the sensing device 2004 can transmit sensor data or monitor data to one or more of the infusion device 2002, the CCD 2006, and / or the computer 2008.

[0189] Still reference Fig. 20 In various embodiments, the sensing device 2004 may be affixed to or embedded in the user's body at a location remote from where the infusion device 2002 is affixed to the user's body. In various other embodiments, the sensing device 2004 may be incorporated into the infusion device 2002. In other embodiments, the sensing device 2004 may be separate and apart from the infusion device 2002 and may be part of the CCD 2006, for example. In such embodiments, the sensing device 2004 may be configured to receive a biological sample, an analyte, etc. to measure a condition of the user.

[0190] In some embodiments, the CCD 2006 and / or the computer 2008 may include electronic devices and other components configured to perform processing, deliver daily doses, and control the infusion device 2002 in a manner affected by the sensor data measured by the sensing device 2004 and / or received therefrom. By including the control functions in the CCD 2006 and / or the computer 2008, the infusion device 2002 can be made of more simplified electronic devices. However, in other embodiments, the infusion device 2002 may include all control functions and may operate without the CCD 2006 and / or the computer 2008. In various embodiments, the CCD 2006 may be a portable electronic device. In addition, in various embodiments, the infusion device 2002 and / or the sensing device 2004 may be configured to transmit data to the CCD 2006 and / or the computer 2008 to display or process the data through the CCD 2006 and / or the computer 2008.

[0191] In some embodiments, CCD 2006 and / or computer 2008 may provide information to the user that facilitates the user's subsequent use of infusion device 2002. For example, CCD 2006 may provide information to the user to allow the user to determine the rate or dosage of the drug to be applied to the user's body. In other embodiments, CCD 2006 may provide information to infusion device 2002 to autonomously control the rate or dosage of the drug applied to the user's body. In some embodiments, sensing device 2004 may be integrated into CCD 2006. Such embodiments may allow the user to assess his or her condition by, for example, providing his or her blood sample to sensing device 2004 to monitor the condition. In some embodiments, sensing device 2004 and CCD 2006 may be used to determine the glucose level in the user's blood and / or body fluids without using or requiring a wire or cable connection between infusion device 2002 and sensing device 2004 and / or CCD 2006.

[0192] In some embodiments, sensing device 2004 and / or infusion device 2002 are cooperatively configured to utilize closed loop system to deliver fluid to user. Examples of sensing devices and / or infusion pumps utilizing closed loop system can be found in, but are not limited to, the following U.S. Patents: No. 6,088,608, No. 6,119,028, No. 6,589,229, No. 6,740,072, No. 6,827,702, No. 7,323,142 and No. 7,402,153, or U.S. Patent Application Publication No. 2014 / 0066889, all of which are incorporated herein by reference in their entirety. In such embodiments, sensing device 2004 is configured to sense or measure the condition of the user such as blood sugar level, etc. Infusion device 2002 is configured to deliver fluid in response to the condition sensed by sensing device 2004. In turn, the sensing device 2004 continues to sense or otherwise quantify the user's current condition, thereby allowing the infusion device 2002 to continue delivering fluid indefinitely in response to the condition currently (or most recently) sensed by the sensing device 2004. In some embodiments, the sensing device 2004 and / or the infusion device 2002 may be configured to utilize the closed-loop system only during a portion of the day, such as only when the user is asleep or awake.

[0193] Figure 21 to Figure 23 An exemplary embodiment of a fluid infusion device 2100 (or alternatively, an infusion pump) suitable for use in an infusion system is shown, for example Figure 6 The infusion device 602 in the infusion system 600 or Fig. 20 The infusion device 2002 in the infusion system 2000 of the present invention. The fluid infusion device 2100 is a portable medical device designed to be carried or worn by a patient (or user), and the fluid infusion device 2100 can utilize any number of conventional features, components, elements and characteristics of existing fluid infusion devices, such as, for example, some of the features, components, elements and / or characteristics described in U.S. Patent Nos. 6,485,465 and 7,621,893. It should be understood that Figure 21 to Figure 23 Certain aspects of the infusion device 2100 are shown in a simplified manner; in practice, the infusion device 2100 may include additional elements, features, or components not shown or described in detail herein.

[0194] like Figure 21 to Figure 22As shown, the exemplary embodiment of the fluid infusion device 2100 includes a housing 2102 adapted to receive a reservoir 2105 containing a fluid. An opening 2120 in the housing 2102 accommodates an accessory 2123 (or cap) for the reservoir 2105, wherein the accessory 2123 is configured to cooperate with or otherwise connect to a conduit 2121 of an infusion set 2125 to provide a fluid path to / from the user's body. In this way, fluid communication from the interior of the reservoir 2105 to the user is established via the conduit 2121. The illustrated fluid infusion device 2100 includes a human-machine interface (HMI) 2130 (or user interface), which includes elements 2132, 2134 that can be manipulated by a user to administer a bolus of a fluid (e.g., insulin), change treatment settings, change user preferences, select display features, etc. The infusion device also includes a display element 2126 such as a liquid crystal display (LCD) or another suitable display element, which can be used to present various types of information or data to the user, such as, but not limited to: the patient's current glucose level; the time; a graph or chart of the patient's glucose level relative to time; a device status indicator, etc.

[0195] The shell 2102 is formed of a substantially rigid material and has a hollow interior space 2114 suitable for allowing electronic components 2104, a sliding member (or sliding part) 2106, a drive system 2108, a sensor assembly 2110 and a drive system cover member 2112 in addition to the reservoir 2105 to be arranged therein, wherein the contents of the shell 2102 are enclosed by a shell cover member 2116. The opening 2120, the slide 2106, and the drive system 2108 are coaxially aligned in an axial direction (indicated by arrow 2118), whereby the drive system 2108 facilitates linear displacement of the slide 2106 in the axial direction 2118 to dispense fluid from the reservoir 2105 (after the reservoir 2105 has been inserted into the opening 2120), wherein the sensor assembly 2110 is configured to measure an axial force (e.g., a force aligned with the axial direction 2118) applied to the sensor assembly 2110 in response to operating the drive system 2108 to displace the slide 2106. In various embodiments, the sensor assembly 2110 can be used to detect one or more of the following: slowing, preventing, or otherwise reducing a blockage in the fluid path of fluid delivery from the reservoir 2105 to the user's body; when the reservoir 2105 is empty; when the slide 2106 is properly seated with the reservoir 2105; when a dose of fluid has been delivered; when the infusion pump 2100 is subjected to shock or vibration; or when the infusion pump 2100 requires maintenance.

[0196] Depending on the embodiment, the reservoir 2105 containing the fluid may be implemented as a syringe, a vial, a cartridge, a bag, etc. In certain embodiments, the infused fluid is insulin, but many other fluids may be administered by infusion, such as, but not limited to, HIV medications, medications for treating pulmonary hypertension, iron chelation medications, pain medications, anti-cancer treatments, vitamins, hormones, etc. Figure 22 to Figure 23 As best shown, the reservoir 2105 generally includes a reservoir barrel 2119 that contains fluid and is concentrically and / or coaxially (e.g., in an axial direction 2118) aligned with the slider 2106 when the reservoir 2105 is inserted into the infusion pump 2100. The end of the reservoir 2105 proximate the opening 2120 may include or otherwise cooperate with a fitting 2123 that secures the reservoir 2105 in the housing 2102 and prevents displacement of the reservoir 2105 relative to the housing 2102 in the axial direction 2118 after the reservoir 2105 is inserted into the housing 2102. As described above, the fitting 2123 extends from (or through) the opening 2120 of the housing 2102 and cooperates with the tubing 2121 to establish fluid communication from the interior of the reservoir 2105 (e.g., the reservoir barrel 2119) to the user via the tubing 2121 and the infusion set 2125. The opposite end of the reservoir 2105 proximal to the slider 2106 includes a plunger 2117 (or plug) positioned to push fluid from the interior of the barrel 2119 of the reservoir 2105 to the user along the fluid path through the conduit 2121. The slider 2106 is configured to mechanically couple or otherwise engage with the plunger 2117 so as to become disposed with the plunger 2117 and / or the reservoir 2105. When the drive system 2108 is operated to displace the slider 2106 in the axial direction 2118 toward the opening 2120 in the housing 2102, the fluid is forced to flow out of the reservoir 2105 via the conduit 2121.

[0197] exist Figure 22 to Figure 23In the illustrated embodiment, the drive system 2108 includes a motor assembly 2107 and a drive screw 2109. The motor assembly 2107 includes a motor coupled to a drive train component of the drive system 2108, which is configured to convert the rotational motor motion into a translational displacement of the slider 2106 in an axial direction 2118, and thereby engage and displace the plunger 2117 of the reservoir 2105 in the axial direction 2118. In some embodiments, the motor assembly 2107 can also be powered to translate the slider 2106 in an opposite direction (e.g., opposite to the direction 2118) to retract and / or remove from the reservoir 2105 to allow replacement of the reservoir 2105. In an exemplary embodiment, the motor assembly 2107 includes a brushless direct current (BLDC) motor having one or more permanent magnets mounted, attached, or otherwise disposed on its rotor. However, the subject matter described herein is not necessarily limited to use with a BLDC motor, and in alternative embodiments, the motor may be implemented as a solenoid motor, an AC motor, a stepper motor, a piezoelectric track drive, a shape memory actuator drive, an electrochemical gas cell, a thermally driven gas cell, a bimetallic actuator, etc. The drive train components may include one or more lead screws, cams, pawls, jacks, pulleys, pawls, clamps, gears, nuts, slides, bearings, levers, beams, stops, plungers, slides, brackets, rails, bearings, supports, bellows, caps, diaphragms, bags, heaters, etc. In this regard, while the illustrated embodiment of the infusion pump uses a coaxially aligned drive train, the motor may be offset relative to the longitudinal axis of the reservoir 2105 or arranged in an otherwise non-coaxial manner.

[0198] like Fig.23As best shown, the drive screw 2109 cooperates with the threads 2302 inside the slider 2106. When the motor assembly 2107 is powered and operated, the drive screw 2109 rotates and forces the slider 2106 to translate in the axial direction 2118. In an exemplary embodiment, the infusion pump 2100 includes a sleeve 2111 to prevent the slider 2106 from rotating when the drive screw 2109 of the drive system 2108 rotates. Therefore, the rotation of the drive screw 2109 causes the slider 2106 to extend or retract relative to the drive motor assembly 2107. When the fluid infusion device is assembled and operable, the slider 2106 contacts the plunger 2117 to engage the reservoir 2105 and control the delivery of fluid from the infusion pump 2100. In an exemplary embodiment, the shoulder portion 2115 of the slider 2106 contacts or otherwise engages the plunger 2117 to displace the plunger 2117 in the axial direction 2118. In an alternative embodiment, the slider 2106 can include a threaded tip 2113 that can be removably engaged with internal threads 2304 on a plunger 2117 of the reservoir 2105, as described in detail in U.S. Patent Nos. 6,248,093 and 6,485,465, which are incorporated herein by reference.

[0199] like Fig. 22 As shown, the electronic assembly 2104 includes a control electronic device 2124 coupled to a display element 2126, wherein the housing 2102 includes a transparent window portion 2128 aligned with the display element 2126 to allow a user to view the display 2126 when the electronic assembly 2104 is disposed in the interior 2114 of the housing 2102. The control electronic device 2124 generally represents hardware, firmware, processing logic and / or software (or a combination thereof) configured to control the operation of the motor assembly 2107 and / or the drive system 2108. Whether such functionality is implemented as hardware, firmware, state machine or software depends on the specific application and design constraints imposed on the embodiment. Those concepts similar to those described herein can implement such functionality in a manner suitable for each specific application, but such specific implementation decisions should not be interpreted as being limited or restrictive. In an exemplary embodiment, the control electronic device 2124 includes one or more programmable controllers that can be programmed to control the operation of the infusion pump 2100.

[0200] The motor assembly 2107 includes one or more electrical leads 2136 adapted to be electrically coupled to the electronic assembly 2104 to establish communication between the control electronics 2124 and the motor assembly 2107. In response to a command signal from the control electronics 2124 that operates a motor driver (e.g., a power converter) to adjust the amount of power supplied to the motor from the power source, the motor actuates the drive train components of the drive system 2108 to displace the slider 2106 in the axial direction 2118, forcing the fluid to flow out of the reservoir 2105 along the fluid path (including the tubing 2121 and the infusion pump), thereby administering a dose of the fluid contained in the reservoir 2105 to the user's body. Preferably, the power source is implemented as one or more batteries contained within the housing 2102. Alternatively, the power source may be a solar panel, a capacitor, an alternating current or a direct current supplied through a power cord, and the like. In some embodiments, the control electronics 2124 may operate the motor assembly 2107 and / or the motor of the drive system 2108 in a stepwise manner, typically on an intermittent basis; administering separate, precise doses of fluid to the user according to a programmed delivery profile.

[0201] refer to Figure 21 to Figure 23As described above, the user interface 2130 includes HMI elements such as buttons 2132 and arrow keys 2134 formed on a graphical keypad overlay 2131 that covers a keypad assembly 2133, which includes features corresponding to the buttons 2132, arrow keys 2134, or other user interface items indicated by the graphical keypad overlay 2131. When assembled, the keypad assembly 2133 is coupled to the control electronics 2124, thereby allowing a user to manipulate the HMI elements 2132, 2134 to interact with the control electronics 2124 and control the operation of the infusion pump 2100, such as to administer a bolus of insulin, change therapy settings, change user preferences, select display features, set or disable alarms and reminders, etc. In this regard, the control electronics 2124 maintains and / or provides information to the display 2126 regarding program parameters, delivery curves, pump operation, alarms, warnings, status, etc. that can be adjusted using the HMI elements 2132, 2134. In various embodiments, the HMI elements 2132, 2134 may be implemented as physical objects (e.g., buttons, knobs, joysticks, etc.) or virtual objects (e.g., using touch sensing and / or proximity sensing technology). For example, in some embodiments, the display 2126 may be implemented as a touch screen or touch-sensitive display, and in such embodiments, the features and / or functionality of the HMI elements 2132, 2134 may be integrated into the display 2126, and the HMI 2130 may not be present. In some embodiments, the electronic assembly 2104 may also include an alarm generating element that is coupled to the control electronics 2124 and is appropriately configured to generate one or more types of feedback, such as, but not limited to: auditory feedback, visual feedback, tactile (physical) feedback, etc.

[0202] refer to Figure 22 to Figure 23According to one or more embodiments, the sensor assembly 2110 includes a back plate structure 2150 and a loading element 2160. The loading element 2160 is disposed between the cover member 2112 and the beam structure 2170, which includes one or more beams having sensing elements disposed thereon, which are affected by a compressive force applied to the sensor assembly 2110 that deflects the one or more beams, as described in more detail in U.S. Pat. No. 8,474,332, which is incorporated herein by reference. In an exemplary embodiment, the back plate structure 2150 is attached, adhered, mounted, or otherwise mechanically coupled to the bottom surface 2138 of the drive system 2108, so that the back plate structure 2150 is located between the bottom surface 2138 of the drive system 2108 and the housing cover 2116. The profile of the drive system cover member 2112 is formed to accommodate and match the bottom of the sensor assembly 2110 and the drive system 2108. A drive system capping member 2112 may be attached to the interior of the housing 2102 to prevent displacement of the sensor assembly 2110 in a direction opposite to the direction of the force provided by the drive system 2108 (e.g., opposite to direction 2118). Thus, the sensor assembly 2110 is positioned between and secured by the motor assembly 2107 and the capping member 2112, which prevents displacement of the sensor assembly 2110 in a downward direction opposite to the direction of arrow 2118, such that when the drive system 2108 and / or the motor assembly 2107 are operated to displace the slider 2106 in an axial direction 2118 opposite to the fluid pressure in the reservoir 2105, the sensor assembly 2110 is subjected to a reactive compressive force. Under normal operating conditions, the compressive force applied to the sensor assembly 2110 is related to the fluid pressure in the reservoir 2105. As shown, the electrical lead 2140 is suitable for electrically coupling the sensing element of the sensor assembly 2110 to the electronic assembly 2104 to establish communication with the control electronic device 2124, wherein the control electronic device 2124 is configured to measure, receive or otherwise obtain an electrical signal from the sensing element of the sensor assembly 2110, which is indicative of the force applied by the drive system 2108 in the axial direction 2118.

[0203] Fig.24An exemplary embodiment of a patient monitoring system 2400 suitable for use with the subject matter described herein is depicted. The patient monitoring system 2400 includes a medical device 2402 communicatively coupled to a sensing element 2404 that is inserted into the body of a patient or worn by the patient to obtain measurement data indicative of a physiological condition in the patient's body, such as a sensed glucose level. The medical device 2402 is communicatively coupled to a client device 2406 via a communications network 2410, wherein the client device 2406 is communicatively coupled to a remote device 2414 via another communications network 2412. In this regard, the client device 2406 can serve as an intermediary for uploading or otherwise providing measurement data from the medical device 2402 (e.g., the server 102) to the remote device 2414. It should be understood that for purposes of illustration, Fig.24 A simplified representation of a patient monitoring system 2400 is shown and is not intended to limit the subject matter described herein in any way.

[0204] In an exemplary embodiment, the client device 2406 is implemented as a mobile phone, a smart phone, a tablet computer or other similar mobile electronic device; however, in other embodiments, the client device 2406 can be implemented as any kind of electronic device capable of communicating with the medical device 2402 via the network 2410, such as a laptop or notebook computer, a desktop computer, etc. In an exemplary embodiment, the network 2410 is implemented as a Bluetooth network, a ZigBee network, or other suitable personal area network. That is, in other embodiments, the network 2410 can be implemented as a wireless ad hoc network, a wireless local area network (WLAN) or a local area network (LAN). The client device 2406 includes a display device (such as a monitor, a screen or another conventional electronic display) or is coupled to a display device that can graphically present data and / or information related to the patient's physiological condition. The client device 2406 also includes a user input device (such as a keyboard, a mouse, a touch screen, etc.) or is otherwise associated with a user input device that can receive input data and / or other information from a user of the client device 2406.

[0205] In an exemplary embodiment, a user (such as a patient, a patient's doctor, or another healthcare provider) manipulates a client device 2406 to execute a client application 2408 that supports communication with a medical device 2402 via a network 2410. In this regard, the client application 2408 supports establishing a communication session with the medical device 2402 over the network 2410, and receiving data and / or information from the medical device 2402 via the communication session. The medical device 2402 may similarly execute or otherwise implement a corresponding application or process that supports establishing a communication session with the client application 2408. The client application 2408 generally represents a software module or another feature generated or otherwise implemented by the client device 2406 to support the processes described herein. Therefore, the client device 2406 generally includes a processing system and a data storage element (or memory) capable of storing programming instructions for execution by the processing system, which, when read and executed, causes the processing system to create, generate, or otherwise facilitate the client application 2408 and perform or otherwise support the processes, tasks, operations, and / or functions described herein. Depending on the embodiment, the processing system can be implemented using any suitable processing system and / or device, such as one or more processors, central processing units (CPUs), controllers, microprocessors, microcontrollers, processing cores and / or other hardware computing resources configured to support the operation of the processing system described herein. Similarly, the data storage element or memory can be implemented as a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic or optical mass storage, or any other suitable non-transitory short-term or long-term data storage or other computer-readable medium and / or any suitable combination thereof.

[0206] In one or more embodiments, the client device 2406 and the medical device 2402 establish an association (or pairing) with each other through the network 2410 to support the subsequent establishment of a point-to-point or peer-to-peer communication session between the medical device 2402 and the client device 2406 via the network 2410. For example, according to one embodiment, the network 2410 is implemented as a Bluetooth network, wherein the medical device 2402 and the client device 2406 are paired with each other by performing a discovery process or other suitable pairing process (e.g., by obtaining and storing each other's network identification information). The pairing information obtained during the discovery process allows either the medical device 2402 or the client device 2406 to initiate the establishment of a secure communication session via the network 2410.

[0207] In one or more exemplary embodiments, the client application 2408 is also configured to store or otherwise maintain the address and / or other identification information of the remote device 2414 on the second network 2412. In this regard, the second network 2412 can be physically and / or logically different from the network 2410, such as the Internet, a cellular network, a wide area network (WAN), etc. The remote device 2414 generally represents a server or other computing device configured to receive and analyze or otherwise monitor measurement data, event log data, and possible other information obtained for patients associated with the medical device 2402. In an exemplary embodiment, the remote device 2414 is coupled to a database 2416 (e.g., database 104), which is configured to store or otherwise maintain data associated with individual patients. In practice, the remote device 2414 can reside in a location physically different and / or separate from the medical device 2402 and the client device 2406, for example, located at a facility owned by the manufacturer of the medical device 2402 and / or operated or otherwise affiliated with the manufacturer. For purposes of illustration, but not limitation, remote device 2414 may optionally be referred to herein as a server.

[0208] still Fig.24 , sensing element 2404 generally represents a component of patient monitoring system 2400 configured to generate, produce, or otherwise output one or more electrical signals indicative of a physiological condition sensed, measured, or otherwise quantified by sensing element 2404. In this regard, the physiological condition of the user will affect the characteristics of the electrical signal output by sensing element 2404, such that the characteristics of the output signal correspond to or are otherwise related to the physiological condition to which sensing element 2404 is sensitive. In an exemplary embodiment, sensing element 2404 is implemented as an interstitial glucose sensing element inserted into a location on the patient's body, which generates an output electrical signal having an electric current (or voltage) associated therewith, the electric current (or voltage) being related to the interstitial fluid glucose level sensed or otherwise measured in the patient's body by sensing element 2404.

[0209] Medical device 2402 generally represents a component of patient monitoring system 2400 that is communicatively coupled to the output of sensing element 2404 to receive or otherwise obtain measurement data samples (e.g., measured glucose and characteristic impedance values) from sensing element 2404, store or otherwise maintain the measurement data samples, and upload or otherwise transmit the measurement data to server 2414 via client device 2406. In one or more embodiments, medical device 2402 is implemented as an infusion device 602, 2002, which is configured to deliver a fluid such as insulin to a patient's body. That is, in other embodiments, medical device 2402 can be a separate and independent stand-alone sensing or monitoring device from an infusion device (e.g., sensing device 604, 2004). It should be noted that although Fig.24 Medical device 2402 and sensing element 2404 are depicted as separate components, but in practice, medical device 2402 and sensing element 2404 may be integrated or otherwise combined to provide an integral device that may be worn by a patient.

[0210] In an exemplary embodiment, the medical device 2402 includes a control module 2422, a data storage element 2424 (or memory) and a communication interface 2426. The control module 2422 generally represents the hardware, circuit, logic, firmware and / or one or more other components of the medical device 2402, which is coupled to the sensing element 2404 to receive the electrical signal output by the sensing element 2404 and perform or otherwise support various additional tasks, operations, functions and / or processes described herein. Depending on the embodiment, the control module 2422 can be implemented or realized using a general-purpose processor, microprocessor, controller, microcontroller, state machine, content addressable memory, application specific integrated circuit, field programmable gate array, any suitable programmable logic device, discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. In some embodiments, the control module 2422 includes an analog-to-digital converter (ADC) or another similar sampling structure, which samples the output electrical signal received from the sensing element 2404 or otherwise converts it into a corresponding digital measurement data value. In other embodiments, the sensing element 2404 may be coupled with an ADC and output digital measurements.

[0211] The communication interface 2426 generally represents the hardware, circuitry, logic, firmware, and / or other components of the medical device 2402 that are coupled to the control module 2422 for outputting data and / or information from / to the medical device 2402 to / from the client device 2406. For example, the communication interface 2426 may include or otherwise be coupled to one or more transceiver modules capable of supporting wireless communication between the medical device 2402 and the client device 2406. In an exemplary embodiment, the communication interface 2426 is implemented as a Bluetooth transceiver or adapter that is configured to support Bluetooth Low Energy (BLE) communications.

[0212] In an exemplary embodiment, the remote device 2414 receives measurement data values ​​(e.g., sensor glucose measurements, acceleration measurements, etc.) associated with a particular patient obtained using the sensing element 2404 from the client device 2406, and the remote device 2414 stores or otherwise maintains the historical measurement data in a database 2416 associated with the patient (e.g., using one or more unique patient identifiers). In addition, the remote device 2414 may also receive meal data or other event log data that may be input or otherwise provided by the patient (e.g., via the client application 2408) from or via the client device 2406, and store or otherwise maintain historical meal data and other historical event or activity data associated with the patient in the database 2416. In this regard, the meal data includes, for example, a time or timestamp associated with a particular meal event, a meal type or other information indicating the composition or nutritional characteristics of the meal, and an indication of the size associated with the meal. In an exemplary embodiment, the remote device 2414 also receives historical fluid delivery data corresponding to the basis or bolus dose of the fluid delivered to the patient by the infusion device 602,2002. For example, the client application 2408 can communicate with the infusion device 602,2002 to obtain the insulin delivery dose and the corresponding timestamp from the infusion device 602,2002, and then upload the insulin delivery data to the remote device 2414 for storage associated with a specific patient. The remote device 2414 can also receive the geolocation data and possible other background data associated with the device 2402,2406 from the client device 2406 and / or the client application 2408, and store or otherwise maintain the historical operation background data associated with the specific patient. In this regard, one or more of the devices 2402,2406 can include a global positioning system (GPS) receiver, or can output in real time or otherwise provide similar modules, components or circuits that characterize the data of the geographical location of the corresponding device 2402,2406.

[0213] As described above, in one or more exemplary embodiments, remote device 2414 utilizes machine learning to determine which combination of variables, fields or attributes of historical observation patient data is associated with or predicts the occurrence of a specific event, activity or metric of a specific patient, and then determines the corresponding equation, function or model for calculating the value of the parameter of interest according to the group of input variables. Therefore, the resulting model can characterize one or more specific combinations of current (or recent) sensor glucose measurement data, auxiliary measurement data, conveying data, geographic location, patient behavior or activity, etc., or map the specific combination to the value representing the current probability or possibility of a specific event or activity or the current value of the parameter of interest. It should be noted that, since the physiological response of each patient may be different from other groups, when modeling is performed by the patient, the subset of input variables predicted or associated with a specific patient may be different from other users. In addition, in such embodiments, based on the different correlations between the specific input variables and the historical data of the specific patient, the relative weight applied for the corresponding variable of the prediction subset can also be different from that of other patients who may have a common prediction subset. It should be noted that the remote device 2414 may utilize any number of different machine learning techniques to determine which input variables predict the patient of current interest, such as artificial neural networks, genetic programming, support vector machines, Bayesian networks, probabilistic machine learning models or other Bayesian techniques, fuzzy logic, combinations of heuristic derivations, and the like.

[0214] Overview of Diabetes Data Management System

[0215] Fig.25 A computing device 2500 suitable for use as part of a diabetes data management system is shown, which incorporates one or more of the processes described above. In some embodiments, the diabetes data management system (DDMS) may be called the Medtronic MiniMed CARELINK TM A DDMS is a medical data management system (MDMS). A DDMS may be built on a server or servers that can be accessed by users or healthcare professionals via a communication network via the Internet or the World Wide Web. Some models of DDMS described as MDMS are described in U.S. Patent Application Publications 2006 / 0031094 and 2013 / 0338630, which are incorporated herein by reference in their entirety.

[0216] Although embodiments are described with respect to monitoring the medical or biological condition of subjects having diabetes, the systems and processes herein are applicable to monitoring the medical or biological condition of subjects with heart disease, cancer, HIV, subjects with other diseases, infections or manageable conditions, or various combinations thereof.

[0217] In an embodiment of the present invention, the DDMS may be installed in a computing device in a healthcare provider's office (e.g., a doctor's office, a nurse's office, a clinic, an emergency room, an emergency room). Healthcare providers may be reluctant to use a system in which their confidential patient data will be stored in a computing device (e.g., a server on the Internet).

[0218] DDMS can be installed on computing device 2500. Computing device 2500 can be coupled to display 2533. In some embodiments, computing device 2500 can be located in a physical device separate from the display (e.g., located in a personal computer, microcomputer, etc.). In some embodiments, computing device 2500 can be located in a single physical housing or a device with display 2533 (e.g., a laptop computer in which display 2533 is integrated into the computing device). In an embodiment of the present invention, computing device 2500 hosting DDMS can be, but is not limited to, a desktop computer, a laptop computer, a server, a network computer, a personal digital assistant (PDA), a portable phone including computer functions, a pager with a large visual display, an insulin pump including a display, a glucose sensor including a display, a glucose meter including a display, and / or a combination insulin pump / glucose sensor with a display. The computing device can also be an insulin pump connected to a display, a glucose meter connected to a display, or a glucose sensor connected to a display. Computing device 2500 can also be a server located on the Internet, which can be accessed through a browser installed on a laptop computer, a desktop computer, a network computer, or a PDA. Computing device 2500 may also be a server located in a doctor's office that can be accessed via a browser installed on a portable computing device (e.g., a laptop computer, a PDA, a network computer, a portable phone) that has wireless capabilities and can communicate via wireless communication protocols (e.g., Bluetooth and IEEE 802.11 protocols).

[0219] exist Fig.25 In the illustrated embodiment, the data management system 2516 includes a set of related software modules or layers that specialize in handling different tasks. The system software includes a device communication layer 2524, a data parsing layer 2526, a database layer 2528, a database storage device 2529, a reporting layer 2530, a graphics display layer 2531, and a user interface layer 2532. The diabetes data management system can be connected to multiple subject support devices 2512 ( Fig.2525 (two of which are shown in FIG). Although different reference numerals refer to multiple layers (e.g., device communication layer, data parsing layer, database layer), each layer may include a single software module or multiple software modules. For example, device communication layer 2524 may include multiple interactive software modules, libraries, etc. In an embodiment of the present invention, data management system 2516 can be installed in a non-volatile storage area (such as a flash memory, hard disk, mobile hard disk, DVD-RW, CD-RW memory) of computing device 2500. If data management system 2516 is selected or started, system 2516 can be loaded into a volatile storage device (such as a memory of DRAM, SRAM, RAM, DDRAM) for execution.

[0220] The device communication layer 2524 is responsible for interacting with at least one, and in other embodiments with multiple different types of subject support devices 2512 (e.g., blood glucose meters, glucose sensors / monitors, or infusion pumps). In one embodiment, the device communication layer 2524 can be configured to communicate with a single type of subject support device 2512. However, in a more comprehensive embodiment, the device communication layer 2524 is configured to communicate with multiple different types of subject support devices 2512 (e.g., devices manufactured by multiple different manufacturers), multiple different models from a specific manufacturer, and / or multiple different devices providing different functions (e.g., infusion functions, sensing functions, metering functions, communication functions, user interface functions, or combinations thereof). By providing the ability to interact with multiple different types of subject support devices 2512, the diabetes data management system 2516 can collect data from significantly more discrete sources. Such embodiments can provide extended and improved data analysis functions by including more subjects and subject groups in the form of statistics or other analysis that can benefit from a larger amount of sample data and / or a greater diversity of sample data, thereby improving the ability to determine appropriate treatment parameters, diagnosis, etc.

[0221] The device communication layer 2524 allows the DDMS 2516 to receive information from each subject support device 2512 in the system 2516 and transmit information thereto. Depending on the implementation scheme and the context of use, the types of information that can be transmitted between the system 2516 and the device 2512 may include, but are not limited to, data, programs, updated software, educational materials, warning messages, notifications, device settings, treatment parameters, etc. The device communication layer 2524 may include a suitable routine for detecting the type of subject support device 2512 communicating with the system 2516 and implementing an appropriate communication protocol for the type of device 2512. Alternatively or in addition thereto, the subject support device 2512 may transmit information in a packet or other data arrangement manner, wherein the communication includes a preamble or includes other parts of device identification information for identifying the type of subject support device. Alternatively or in addition thereto, the subject support device 2512 may include a suitable user-operable interface to allow the user to input information corresponding to the type of subject support device used by the user (e.g., by selecting an optional icon or text or other device identifier). This type of information may be transmitted to the system 2516 via a network connection. In further embodiments, system 2516 may detect the type of subject support device 2512 with which it is communicating in the manner described above, and may then send a message requesting the user to verify that system 2516 correctly detected the type of subject support device the user is using. For systems 2516 that are capable of communicating with multiple different types of subject support devices 2512, device communication layer 2524 is capable of implementing multiple different communication protocols and selecting a protocol appropriate for the type of subject support device detected.

[0222] The data parsing layer 2526 is responsible for verifying the integrity of the received device data and correctly inputting it into the database 2529. A cyclic redundancy check CRC process for checking the integrity of the received data can be used. Alternatively or in addition, the data can be received in packets or other data arrangements, wherein the preamble or other portion of the data includes device type identification information. Such a preamble or other portion of the received data may also include a device serial number or other identification information that can be used to verify the authenticity of the received information. In such embodiments, the system 2516 can compare the received identification information with pre-stored information to assess whether the received information is from a valid source.

[0223] The database layer 2528 may include a centralized database repository that is responsible for warehousing and archiving stored data in an ordered format for later access and retrieval. The database layer 2528 works with one or more data storage devices 2529 that are suitable for storing and providing access to data in the manner described herein. Such data storage devices 2529 may include, for example, one or more hard disks, optical disks, magnetic tapes, digital libraries, or other suitable digital or analog storage media and associated drive devices, drive arrays, etc.

[0224] Data can be stored and archived for various purposes, depending on the implementation scheme and the environment of use. Information about specific subjects and patient support devices can be stored and archived and can be used by these specific subjects, their authorized medical service providers and / or authorized medical service payment entities to analyze the conditions of the subjects. In addition, certain information about groups of subjects or groups of subject support devices can be used more generally for medical service providers, subjects, personnel of entities managing system 2516, or other entities to analyze group data or other forms of comprehensive data.

[0225] Embodiments of the database layer 2528 and other components of the system 2516 may employ appropriate data security measures to protect the subject's personal medical information while also allowing non-personal medical information to be more generally available for analysis. Embodiments may be configured to comply with appropriate government regulations, industry standards, policies, etc., including, but not limited to, the Health Insurance Portability and Accountability Act of 1996 (HIPAA).

[0226] The database layer 2528 may be configured to restrict each user's access to the type of information that is pre-authorized for that user. For example, a subject may be allowed to access his or her personal medical information (using a personal identifier) ​​stored by the database layer 2528, but not to access the personal medical information (using a personal identifier) ​​of other subjects. Similarly, the subject's authorized medical service provider or payment entity may be provided with access rights to some or all of the subject's personal medical information (using an individual identifier) ​​stored by the database layer 2528, but not to access the personal information of others. Moreover, an operator or administrator user (on a separate computer that communicates with the computing device 2500) may be provided with access rights to some or all of the subject's information, depending on the role of the operator or administrator. On the other hand, a subject, a medical service provider, an operator, an administrator, or other entities may be authorized to access general information of unidentified individuals, groups, or joint enterprises (without a personal identifier) ​​stored in the data storage device 2529 by the database layer 2528.

[0227] In an exemplary embodiment, database 2529 stores uploaded patient measurement data (e.g., sensor glucose measurement results and characteristic impedance values) and event log data, which includes event records created during the monitoring period corresponding to the measurement data. In an embodiment of the present invention, database layer 2528 can also store preference profiles. In database layer 2528, for example, each user can store information about specific parameters corresponding to the user. For example, these parameters may include target blood sugar or sensor glucose levels, device types (insulin pumps, glucose sensors, blood glucose meters, etc.) used by users, and may be stored in records, files, or memory locations in one or more data storage devices 2529 in the database layer. Preference profiles may include various thresholds, monitoring cycle values, priority criteria, screening criteria, and / or other user-specific values ​​for parameters displayed on display 2533 or support device 2512 in a personalized or patient-specific manner.

[0228] The DDMS 2516 can measure, analyze, and track a user's blood glucose (BG) or sensor glucose (SG) measurements (or readings). In an embodiment of the present invention, the medical data management system can measure, track, or analyze both the user's BG and SG readings. Thus, while certain reports may only refer to or describe BG or SG, the report may monitor and display the results of the other glucose reading or both glucose readings.

[0229] The reporting layer 2530 may include a reporting wizard that extracts data from a selected location in the database 2529 and generates report information based on the desired parameters of interest. The reporting layer 2530 may be configured to generate a plurality of different types of reports, each report having different information and / or displaying information in a different format (layout or style), wherein the type of report may be selected by the user. Reports of a plurality of preset types (with predefined types of content and formats) may be available and selectable to the user. At least some of the reports of the preset types may be common industry standard report types that many healthcare providers should be familiar with. In the exemplary embodiments described herein, the reporting layer 2530 is also conducive to generating snapshot reports including snapshot GUI displays.

[0230] In an embodiment of the present invention, database layer 2528 can calculate values ​​of various medical information that will be displayed on a report or report generated by reporting layer 2530. For example, database layer 2528 can calculate the average blood sugar or sensor glucose reading for a specified time range. In an embodiment of the present invention, reporting layer 2530 can calculate values ​​of medical or physical information that will be displayed on a report. For example, a user can select parameters that are then used by reporting layer 2530 to generate medical information values ​​corresponding to the selected parameters. In other embodiments of the present invention, a user can select a parameter profile that previously existed in database layer 2528.

[0231] Alternatively or in addition, the report wizard can allow the user to design a report of a custom type. For example, the report wizard can allow the user to define and input parameters (for example, specify the content data type, the time period of such data, the parameters of the report format, etc.), and can select data from the database and arrange the data into a printable or displayable arrangement based on the user-defined parameters. In other embodiments, the report wizard can interact with other programs available to the user (for example, common report generation, formatting or statistical analysis programs) or provide data for its use. In this way, the user can import the data from the system 2516 into other reporting tools familiar to the user. The report layer 2530 can allow the user to view the displayable form of the report on a standard display device, the printable form of the report that allows the user to print on a standard printer, or other suitable forms that are convenient for user access to generate reports. The embodiment can be operated with a conventional file format scheme to simplify storage, printing and transmission functions, including but not limited to PDF, JPEG, etc. For example, the user can select the report type and the parameters for the report, and the report layer 2530 can create a report in PDF format. The PDF plug-in can be started to help create a report and allow the user to view the report. Under these operating conditions, the user can use the PDF plug-in to print the report. In certain embodiments implementing security measures, for example, to meet government regulations, industry standards, or policies that restrict the communication of a subject's personal information, some or all reports may be generated via a form (or using an appropriate software control) to prohibit printing or electronic transmission (e.g., in a non-printable and / or unusable format). In other embodiments, the system 2516 may allow a user to generate a report to designate the report as non-printable and / or non-transferable, whereby the system 2516 will provide the report in a form that prohibits printing and / or electronic transmission.

[0232] The reporting layer 2530 may transmit the selected report to the graphic display layer 2531. The graphic display layer 2531 receives information about the selected report and converts the data into a format that can be displayed or shown on the display 2533.

[0233] In an embodiment of the present invention, the reporting layer 2530 can store multiple user parameters. For example, the reporting layer 2530 can store carbohydrate unit types, blood sugar sports or sensor glucose readings, carbohydrate conversion factors, and time ranges for a particular type of report. These examples are intended to illustrate and not limit.

[0234] Data analysis and presentation of the reported information can be used to develop and support diagnostic and therapeutic parameters. When the information in the report is related to an individual subject, diagnostic and therapeutic parameters can be used to assess the health status and relative physical and mental health of the subject, assess the subject's compliance to treatment, and develop or modify the subject's therapy, and assess the subject's behavior that affects his / her treatment. If the information in the report is related to a subject group or data set, diagnostic and therapeutic parameters can be used to assess the health status and relative physical and mental health of a subject group with a similar medical condition, such as but not limited to diabetic subjects, heart disease subjects, diabetic subjects with a specific type of diabetes or heart disease, subjects of a specific age, sex, or other overall statistical group, subjects with symptoms that affect treatment decisions (such as but not limited to pregnancy, obesity, hypoglycemic consciousness disorder, learning disabilities, limited self-care ability, various levels of insulin resistance, combinations thereof, etc.).

[0235] The user interface layer 2532 supports interaction with end users, such as for user login and data access, software navigation, data entry, user selection of desired report types, and display of selected information. Users can also enter parameters to be used in selected reports through the user interface layer 2532. Examples of users include, but are not limited to: healthcare providers, healthcare payment entities, system operators or administrators, researchers, commercial entities, healthcare service agencies and organizations, etc., depending on the services provided by the system and depending on the embodiment of the present invention. A more comprehensive embodiment can interact with some or all of the above types of users, where different types of users can access different services or data or different levels of services or data.

[0236] In an exemplary embodiment, the user interface layer 2532 provides one or more websites that users can access via the Internet. The user interface layer may include or operate with at least one (or more) suitable web servers to provide websites via the Internet and allow global access from Internet-connected computers using standard Internet browser software. Various types of users may access the one or more websites, including but not limited to subjects, medical service providers, researchers, commercial entities, medical service agencies and organizations, payment entities, pharmaceutical partners or other sources of drugs or medical devices and / or support personnel or other personnel operating the system 2516, depending on the embodiment used.

[0237] In another exemplary embodiment, when DDMS 2516 is located on a computing device 2500, the user interface layer 2532 provides a plurality of menus for the user to navigate in DDMS. These menus can be created using any menu format, including but not limited to HTML, XML, or Active Server pages. A user can access DDMS 2516 to perform one or more of a variety of tasks, such as accessing general information available on a website for all subjects or subject groups. The user interface layer 2532 of DDMS 2516 can allow a user to access specific information or generate a report about the subject's medical condition or one or more medical devices 2512 of the subject, transfer data or other information from one or more support devices 2512 of the subject to system 2516, transfer data, programs, program updates, or other information from system 2516 to one or more support devices 2512 of the subject, manually enter information into system 2516, participate in a remote consultation exchange with a healthcare provider, or modify custom settings in the subject's support device and / or the subject's DDMS / MDMS data file.

[0238] System 2516 can provide different users and different types or groups of users with access to different optional resources or activities (including access to different information items and services), so that each user can have a customized experience and / or each type or group of users (e.g., all users, diabetes users, heart disease users, healthcare provider users, or payer users, etc.) can have a set of different information items or services available on the system. System 2516 may include or employ one or more suitable resource configuration programs or systems to allocate appropriate resources to each user or user type based on a predefined authorization plan. Resource configuration systems are well known to be related to configuring electronic office resources (email, licensed software programs, sensitive data, etc.) in an office environment (e.g., a local area network LAN in an office, company, or enterprise). In an exemplary embodiment, such a resource configuration system is suitable for controlling access to medical information and services on DDMS2516 based on the type of user and / or the identity of the user.

[0239] Upon entering successfully authenticated user identification information and password, the user may be provided with access to secure personalized information stored on DDMS 2516. For example, the user may be provided with access to a secure personalized location in DDMS 2516 that has been assigned to the subject. The personalized location may be referred to as a personalized screen, a home screen, a main menu, a personalized page, etc. The personalized location may provide a personalized home screen for the subject, including optional icons or menu items for selecting optional activities, including, for example, options for transferring device data from the subject's support device 2512 to the system 2516, manually entering additional data into the system 2516, modifying the subject's custom settings, and / or viewing and printing reports. The report may include data specific to the subject's condition, including, but not limited to, data obtained from one or more subject support devices 2512 of the subject, manually entered data, data from a medical library or other networked treatment management system, data from a subject or subject group, etc. When the report includes subject-specific information and subject identification information, the report may be generated by some or all of the subject data stored in a secure storage area (e.g., storage device 2529) employed by the database layer 2528.

[0240] The user may select an option for transferring (sending) device data to the medical data management system 2516. If the system 2516 receives a user request to transfer device data to the system, the system 2516 may provide the user with step-by-step instructions on how to transfer data from one or more support devices 2512 of a subject. For example, the DDMS 2516 may have multiple different sets of stored instructions for instructing the user how to download data from different types of subject support devices, where each set of instructions relates to a specific type of subject support device (e.g., pump, sensor, meter, etc.), a specific manufacturer version of a type of subject support device, etc. The registration information received from the user during registration may include information about the type of one or more subject support devices 2512 used by the subject. The system 2516 may use this information to select one or more stored instruction sets associated with one or more support devices 2512 of a particular subject for display to the user.

[0241] Other activities or resources available to the user on system 2516 may include options for manually entering information into DDMS / MDMS 2516. For example, from a user's personalized menu or location, the user may select an option for manually entering additional information into system 2516.

[0242] Additional optional activities or resources may be available to the user of the DDMS 2516. For example, from the user's personalized menu, the user may select an option to receive data, software, software updates, treatment recommendations, or other information from the system 2516 on one or more of the subject's support devices 2512. If the system 2516 receives a request from the user to receive data, software, software updates, treatment recommendations, or other information, the system 2516 may provide the user with a list or other arrangement of a plurality of selectable icons or other indicia representing available data, software, software updates, or other information available to the user.

[0243] On the medical data management system 2516, the user can use additional optional activities or resources, including, for example, options for a personalized location or menu for the user to customize or otherwise further personalize the user. Specifically, from the user's personalized location, the user can select an option for customizing parameters for the user. In addition, the user can create a profile for customizable parameters. When the system 2516 receives such a request from the user, the system 2516 can provide the user with a list or other arrangement of multiple optional icons or other marks representing parameters that can be modified to adapt to the user's preferences. When the user selects one or more icons or other marks, the system 2516 can receive the user's request and make the requested modification.

[0244] In one or more exemplary embodiments, for an individual patient in the DDMS, the computing device 2500 of the DDMS is configured to analyze the patient's historical measurement data, historical delivery data, historical event log data, and any other historical or contextual data associated with the patient maintained in the database layer 2528 to support one or more processes described herein. In this regard, machine learning, artificial intelligence, or similar mathematical modeling of the patient's physiological behavior or response can be performed at the computing device 2500 to facilitate patient-specific correlations or predictions. The result model can be used at the computing device 2500 or another device 2512 of the DDMS to analyze current measurement data, delivery data, and event log data related to the patient and current contextual data to determine predictions or other possible events, behaviors, or results related to the patient in real time.

[0245] In addition, the following exemplary embodiments are provided, which are numbered for ease of reference:

[0246] Embodiment 1: A method for monitoring a patient's physiological condition, the method comprising: obtaining, at a computing device, current measurement data of the patient's physiological condition provided by a sensing device; obtaining, at the computing device, user input indicating one or more future events associated with the patient; and in response to the user input: using one or more prediction models associated with the patient, determining a prediction of the patient's future physiological condition based at least in part on the current measurement data and the one or more future events; and displaying, at the computing device, a graphical representation of the prediction on a display device.

[0247] Embodiment 2: A method according to embodiment 1, wherein determining a prediction comprises: for each corresponding prediction model in a plurality of different prediction models, determining a weighting factor associated with the corresponding prediction model based at least in part on one or more future events; using each of a plurality of different prediction models associated with the patient, determining a plurality of prediction values ​​indicating a future physiological condition based at least in part on current measurement data and one or more future events, wherein each of the plurality of prediction values ​​is associated with a corresponding prediction model in the plurality of different prediction models; and determining the prediction of the patient's physiological condition as a weighted average of the corresponding prediction values ​​in the plurality of prediction values ​​and the weighting factors associated with the corresponding prediction models.

[0248] Embodiment 3: A method according to embodiment 1, wherein determining a prediction comprises: using a first prediction model to determine a first plurality of prediction values ​​of the patient's future physiological condition based at least in part on current measurement data; using a second prediction model different from the first prediction model to determine a second plurality of prediction values ​​of the patient's future physiological condition based at least in part on current measurement data; for each of the first prediction model and the second prediction model, determining a weighting factor associated with the corresponding prediction model based at least in part on one or more future events; and determining a collective prediction of the patient's physiological condition relative to future time based at least in part on the first plurality of prediction values, the second plurality of prediction values, and the weighting factors associated with the corresponding first prediction model and the second prediction model, wherein a graphical representation of the prediction comprises a graphical representation of the collective prediction relative to time.

[0249] Embodiment 4: A method according to embodiment 3, wherein: based on the relationship between a first reliability metric associated with a first prediction model and a second reliability metric associated with a second prediction model, the weighting factor varies relative to an amount of future time; and the first reliability metric and the second reliability metric are affected by one or more future events.

[0250] Embodiment 5: The method of Embodiment 3, wherein determining the first plurality of prediction values ​​comprises determining hourly prediction values ​​of the physiological condition using an hourly prediction model associated with the patient based at least in part on current measurement data and one or more future events.

[0251] Embodiment 6: A method according to embodiment 5, wherein the hourly forecast model comprises: a neural network including a plurality of cells; each cell corresponds to a corresponding hourly interval; and at least one of the plurality of cells is configured to output an average value of the physiological condition during the corresponding hourly interval in the future based at least in part on a subset of one or more future events predicted to occur in the corresponding hourly interval in the future.

[0252] Embodiment 7: The method according to Embodiment 6 further includes obtaining background measurement data from a second sensing device, wherein determining the hourly forecast value includes determining the hourly forecast value based at least in part on current measurement data, one or more future events and the background measurement data.

[0253] Embodiment 8: A method according to Embodiment 5, wherein determining a second plurality of predicted values ​​comprises determining predicted sample values ​​of the physiological condition using at least one of an autoregressive integrated moving average model determined based on historical data associated with the patient, or a physiological model determined based on historical data associated with the patient.

[0254] Embodiment 9: The method according to embodiment 8, wherein the ensemble forecast comprises a weighted average of an hourly forecast value weighted using a first weighting factor and a second plurality of forecast values ​​weighted using a second weighting factor.

[0255] Example 10: A method according to Example 1, wherein: obtaining current measurement data includes receiving sensor glucose measurement data from a glucose sensing device; obtaining user input includes receiving at least one of the following via a user interface at a computing device: an expected amount of carbohydrates to be consumed by the patient, an expected amount of exercise to be performed by the patient, and an expected bolus amount of insulin to be administered; determining a prediction includes using multiple prediction models associated with the patient to determine an aggregate prediction of the patient's future glucose levels based at least in part on the sensor glucose measurement data and one or more future events; and displaying a graphical representation of the prediction includes displaying a graphical representation of the aggregate prediction relative to time.

[0256] Embodiment 11: A method according to Embodiment 10, wherein determining an ensemble prediction comprises: using a prediction model associated with the patient to determine a first plurality of prediction values ​​for the patient's future glucose levels based at least in part on sensor glucose measurement data; using a second prediction model to determine a second plurality of prediction values ​​for the patient's future glucose levels based at least in part on the sensor glucose measurement data; determining a first weighting factor associated with the prediction model based at least in part on one or more future events; determining a second weighting factor associated with the second prediction model based at least in part on one or more future events; and using the first weighting factor and the second weighting factor, determining the ensemble prediction as a weighted average of the first plurality of prediction values ​​and the second plurality of prediction values.

[0257] Embodiment 12: The method according to embodiment 1 further includes providing a graphical user interface display on a display device that prompts a patient for conversational interaction, wherein: obtaining user input includes receiving conversational input from the patient indicating one or more future events; and displaying a graphical representation of the prediction includes providing a graphical representation of the prediction within the graphical user interface display in response to the conversational input.

[0258] Embodiment 13: A computer-readable medium having instructions stored thereon, the instructions being executable by a processing system of a computing device to perform the method according to Embodiment 1.

[0259] Embodiment 14: An electronic device comprising: a communication interface for receiving current measurement data of a patient's physiological condition from a sensing device; a display device having a graphical user interface display presented thereon; a user interface for obtaining user input indicating one or more future events; and a control system coupled to the communication interface, the display device, and the user interface to use one or more predictive models associated with the patient to determine a prediction of the patient's future physiological condition based at least in part on the current measurement data and one or more future events, and to display a graphical representation of the prediction within the graphical user interface display of the display device.

[0260] Embodiment 15: An electronic device according to Embodiment 14, wherein: the prediction includes a weighted average of a first plurality of prediction values ​​determined using a first prediction model and a second plurality of prediction values ​​determined using a second prediction model; and based at least in part on the relationship between one or more future events and a first reliability metric associated with the first prediction model and a second reliability metric associated with the second prediction model, the weighting factors associated with the corresponding first prediction model and the second prediction model vary relative to future time.

[0261] Embodiment 16: An electronic device according to Embodiment 15, wherein the first plurality of predicted values ​​includes predicted hourly averages of the patient's physiological condition.

[0262] Embodiment 17: An electronic device according to Embodiment 14, wherein: current measurement data includes sensor glucose measurement data from a glucose sensing device; one or more future events include at least one of: an expected amount of carbohydrates to be consumed by the patient, an expected amount of exercise to be performed by the patient, and an expected bolus amount of insulin to be administered; and the prediction includes simulated glucose levels determined using multiple prediction models.

[0263] Embodiment 18: According to the electronic device described in Embodiment 14, a graphical user interface display prompts a conversation interaction, wherein: the user input includes a conversation input from the patient indicating one or more future events; and in response to the conversation input, a graphical representation of the prediction is provided within the graphical user interface display.

[0264] Example 19: A method for monitoring a patient's glucose level, the method comprising: obtaining, at a computing device, current sensor glucose measurement data of the patient from a glucose sensing device; obtaining, at the computing device, user input indicating one or more future events for the patient via a user interface; determining, at the computing device, a future simulated glucose level of the patient based at least in part on the current sensor glucose measurement data and one or more future events using a plurality of different prediction models associated with the patient, wherein the plurality of different prediction models include an hourly forecast model associated with the patient; and displaying, on a display device associated with the computing device, a graphical representation of the simulated glucose level relative to future time.

[0265] Embodiment 20: The method according to embodiment 19 further includes determining a weighting factor associated with a corresponding prediction model among multiple different prediction models based at least in part on one or more future events, wherein determining the simulated glucose level includes using the weighting factor to determine a weighted average of multiple predicted glucose values ​​output by multiple different prediction models.

[0266] Embodiment 21: A method for monitoring a patient's physiological condition, the method comprising: obtaining current measurement data of the patient's physiological condition from a sensing device; using a first prediction model to determine a first plurality of prediction values ​​of the patient's future physiological condition based at least in part on the current measurement data; using a second prediction model different from the first prediction model to determine a second plurality of prediction values ​​of the patient's future physiological condition based at least in part on the current measurement data; determining a collective prediction of the patient's physiological condition relative to a future time based at least in part on the first plurality of prediction values, the second plurality of prediction values, and weighting factors associated with the corresponding first prediction model and second prediction models, wherein the weighting factor varies relative to the future time based on a relationship between a first reliability metric associated with the first prediction model and a second reliability metric associated with the second prediction model; and displaying a graphical representation of the collective prediction of the patient's physiological condition relative to the future time on a display device.

[0267] Embodiment 22: The method according to embodiment 21 further includes displaying a graphical representation of current measurement data relative to time at a graphical user interface display on a display device, wherein displaying a graphical indication of a collective forecast includes displaying a graphical representation of a collective forecast in response to user input to adjust the graphical user interface display to view a future time.

[0268] Embodiment 23: The method of Embodiment 21, wherein determining a first plurality of predicted values ​​comprises determining hourly predicted values ​​for the physiological condition using an hourly prediction model associated with the patient.

[0269] Embodiment 24: The method of Embodiment 23, wherein determining a second plurality of predicted values ​​comprises determining predicted sample values ​​of the physiological condition using an autoregressive integrated moving average model determined based on historical data associated with the patient.

[0270] Embodiment 25: The method of Embodiment 23, wherein determining a second plurality of predicted values ​​comprises determining predicted sample values ​​of the physiological condition using a physiological model determined based on historical data associated with the patient.

[0271] Embodiment 26: The method according to Embodiment 23, wherein a first one of the weighting factors associated with the hourly forecast model increases relative to the future amount of time, and a second one of the weighting factors associated with the second forecast model decreases relative to the future amount of time.

[0272] Embodiment 27: The method of Embodiment 26, wherein the ensemble forecast comprises a weighted average of an hourly forecast value weighted using a first weighting factor and a second plurality of forecast values ​​weighted using a second weighting factor.

[0273] Embodiment 28: The method according to embodiment 21 also includes identifying a current operating context; and determining a weighting factor with respect to time based at least in part on the current operating context.

[0274] Embodiment 29: The method of embodiment 21, wherein determining the ensemble prediction comprises using a weighting factor to determine a weighted average of the first plurality of prediction values ​​and the second plurality of prediction values.

[0275] Embodiment 30: A method according to Embodiment 29, wherein: determining a first plurality of prediction values ​​comprises determining hourly prediction values ​​of the physiological condition using an hourly prediction model associated with the patient; determining a second plurality of prediction values ​​comprises determining predicted sample values ​​of the physiological condition using an autoregressive integrated moving average model associated with the patient and one of the physiological models associated with the patient; and the ensemble prediction comprises a weighted average of the hourly prediction values ​​and the second plurality of prediction values.

[0276] Embodiment 31: A computer-readable medium having instructions stored thereon, the instructions being executable by a processing system coupled to a display device to perform the method according to Embodiment 21.

[0277] Embodiment 32: A method for monitoring a patient's physiological condition, the method comprising: obtaining current measurement data of the patient's physiological condition from a sensing device; using a plurality of different prediction models associated with the patient, determining a plurality of prediction values ​​indicating the physiological condition at a future time based at least in part on the current measurement data, wherein each of the plurality of prediction values ​​is associated with a corresponding prediction model among the plurality of different prediction models; for each of the plurality of different prediction models, determining a reliability metric associated with the corresponding prediction model based at least in part on a relationship between the future time and the current time; for each of the plurality of different prediction models, determining a weighting factor associated with the corresponding prediction model based at least in part on the reliability metric associated with the corresponding prediction model; determining a collective prediction value of the patient's physiological condition as a weighted average of the corresponding prediction values ​​among the plurality of prediction values ​​and the weighting factors associated with the corresponding prediction models; and displaying a graphical indication of the collective prediction value of the patient's physiological condition associated with the future time.

[0278] Embodiment 33: A method according to Embodiment 32, wherein the weighting factors associated with the corresponding prediction models vary with respect to time.

[0279] Embodiment 34: According to the method described in Embodiment 32, multiple different prediction models include an hourly forecast model, wherein determining the collective prediction value includes determining a predicted hourly average of the physiological condition associated with the hourly forecast model, and a weighted average of corresponding prediction values ​​among multiple prediction values ​​associated with one or more different prediction models.

[0280] Embodiment 35: A method according to embodiment 32, wherein determining a reliability metric comprises: determining a prediction range corresponding to a future time before a current time of day; obtaining historical data associated with a patient for the prediction range, the historical data comprising historical measurement data of a physiological condition associated with the prediction range; and for each corresponding prediction model among a plurality of different prediction models, determining a reference prediction for the prediction range based on the historical data using the corresponding prediction model; and determining a reliability metric associated with the corresponding prediction model based on a difference between the reference prediction and the historical measurement data.

[0281] Embodiment 36: The method of Embodiment 35, wherein determining the reliability metric comprises determining a mean absolute difference associated with the corresponding prediction model for the prediction horizon.

[0282] Embodiment 37: An electronic device comprises: a communication interface for receiving current measurement data of a patient's physiological condition from a sensing device; a display device having a graphical user interface display including a graphical representation of the current measurement data; a user interface for obtaining user input for adjusting the graphical user interface display to view the future; and a control system coupled to the communication interface, the display device, and the user interface so as to determine, using a first prediction model, a first plurality of prediction values ​​of the patient's future physiological condition based at least in part on the current measurement data; to determine, using a second prediction model different from the first prediction model, a second plurality of prediction values ​​of the patient's future physiological condition based at least in part on the current measurement data; to determine, based at least in part on the first plurality of prediction values ​​and the second plurality of prediction values, a collective prediction of the patient's physiological condition relative to a future time; and to display, on the graphical user interface display, a graphical representation of the collective prediction in response to the user input.

[0283] Embodiment 38: An electronic device according to embodiment 37, wherein: the ensemble prediction includes a weighted average of the first plurality of prediction values ​​and the second plurality of prediction values. And based on the relationship between the first reliability metric associated with the first prediction model and the second reliability metric associated with the second prediction model, the weighting factors associated with the corresponding first prediction model and the second prediction model vary relative to the future time.

[0284] Embodiment 39: An electronic device according to Embodiment 38, wherein the first plurality of predicted values ​​includes predicted hourly averages of the patient's physiological condition.

[0285] Embodiment 40: An electronic device according to Embodiment 39, wherein a first weighting factor of the weighting factors associated with the hourly forecast model increases as the amount of future time increases, and a second weighting factor of the weighting factors associated with the second forecast model decreases as the amount of future time increases.

[0286] Embodiment 41: A method for monitoring a patient's physiological condition, the method comprising: obtaining current measurement data of the patient's physiological condition from a sensing device; predicting one or more events that may affect the patient's physiological condition at one or more different times in the future based at least in part on historical event data associated with the patient; using a predictive model associated with the patient, determining a plurality of predicted values ​​of the patient's physiological condition associated with a plurality of different time periods in the future based at least in part on the current measurement data and one or more events; and displaying the plurality of predicted values ​​relative to a plurality of different time periods in the future on a display device.

[0287] Embodiment 42: The method according to Embodiment 41 further includes determining a prediction model associated with the patient based at least in part on the relationship between historical measurement data of the patient's physiological condition and historical event data associated with the patient.

[0288] Embodiment 43: A method according to embodiment 42, wherein: the prediction model includes a neural network, which includes multiple cells; and each cell corresponds to a corresponding time period among multiple different time periods, and is configured to output an average value of the physiological condition during the corresponding time period in the future based at least in part on a subset of one or more future events predicted to occur in the corresponding time period in the future.

[0289] Embodiment 44: A method according to embodiment 42, wherein determining the prediction model includes determining, for each hourly interval of a plurality of hourly intervals, a corresponding long short-term memory (LSTM) unit based at least in part on the relationship between a corresponding subset of historical measurement data corresponding to the corresponding hourly interval and a corresponding subset of historical event data corresponding to the corresponding hourly interval, the unit being configured to output an average value of the physiological condition during the corresponding hourly interval of the plurality of hourly intervals.

[0290] Embodiment 45: A method according to Embodiment 44, wherein determining multiple forecast values ​​includes, for each corresponding hourly interval in the future, using a corresponding LSTM unit associated with the corresponding hourly interval, calculating a corresponding hourly average forecast value associated with the corresponding hourly interval based at least in part on a subset of one or more events predicted to occur within the corresponding hourly interval.

[0291] Embodiment 46: The method of Embodiment 41 further comprises obtaining background measurement data from a second sensing device, wherein predicting one or more events comprises predicting the one or more events based at le...

Claims

1. A method for monitoring a patient's physiological condition, the method comprising: obtaining current measurements of the patient's physiological condition from a sensing device; determining, using a first prediction model, a first plurality of predicted values ​​for a future physiological condition of the patient based at least in part on the current measurement data, wherein determining the first plurality of predicted values ​​comprises determining hourly forecast values ​​for the physiological condition using an hourly forecast model associated with the patient; determining, using a second prediction model different from the first prediction model, a second plurality of predictions of a future physiological condition of the patient based at least in part on the current measurement data; determining an aggregate prediction of the patient's physiological condition relative to a future time based at least in part on the first plurality of prediction values, the second plurality of prediction values, and weighting factors associated with respective first and second prediction models, wherein the weighting factors vary relative to the future time based on a relationship between a first reliability metric associated with the first prediction model and a second reliability metric associated with the second prediction model; as well as A graphical indication of the collective prediction of the patient's physiological condition relative to a future time is displayed on a display device.

2. The method of claim 1 further comprising displaying, on the display device, a graphical representation of the current measurement data relative to time at a graphical user interface display, wherein displaying the graphical indication of the collective forecast comprises displaying the graphical representation of the collective forecast in response to user input adjusting the graphical user interface display to view a future time.

3. The method of claim 1, wherein determining the second plurality of predicted values ​​comprises determining predicted sample values ​​of the physiological condition using an autoregressive integrated moving average model determined based on historical data associated with the patient. 4 . The method of claim 1 , wherein determining the second plurality of predicted values ​​comprises determining predicted sample values ​​of the physiological condition using a physiological model determined based on historical data associated with the patient.

5. The method of claim 1, wherein a first one of the weighting factors associated with the hourly forecast model increases relative to a future amount of time, and a second one of the weighting factors associated with the second forecast model decreases relative to a future amount of time. 6 . The method of claim 5 , wherein the ensemble forecast comprises a weighted average of an hourly forecast value weighted using the first weighting factor and a second plurality of forecast values ​​weighted using the second weighting factor.

7. The method according to claim 1, further comprising Identify the current operational context; and The weighting factor is determined with respect to time based at least in part on the current operating context.

8. The method of claim 1, wherein determining the ensemble prediction comprises determining a weighted average of the first plurality of prediction values ​​and the second plurality of prediction values ​​using the weighting factor.

9. The method according to claim 8, wherein: Determining the first plurality of predicted values ​​includes determining hourly predicted values ​​for the physiological condition using an hourly forecast model associated with the patient; Determining the second plurality of predicted values ​​includes determining predicted sample values ​​of the physiological condition using one of an autoregressive integrated moving average model associated with the patient and a physiological model associated with the patient; and The ensemble forecast includes a weighted average of the hourly forecast value and the second plurality of forecast values.

10. A computer readable medium having instructions stored thereon, the instructions executable by a processing system coupled to a display device to perform the method of claim 1.

11. A method for monitoring a physiological condition of a patient, the method comprising: obtaining current measurements of the patient's physiological condition from a sensing device; determining, using a plurality of different prediction models associated with the patient, a plurality of prediction values ​​indicative of a physiological condition at a future time based at least in part on the current measurement data, wherein each prediction value of the plurality of prediction values ​​is associated with a respective prediction model of the plurality of different prediction models, the plurality of different prediction models comprising an hourly forecast model; for each prediction model of the plurality of different prediction models, determining a reliability metric associated with the respective prediction model based at least in part on a relationship between the future time and the current time; For each respective prediction model of the plurality of different prediction models, determining a weighting factor associated with the respective prediction model based at least in part on the reliability metric associated with the respective prediction model; Determining an aggregate prediction value of the patient's physiological condition as a weighted average of corresponding prediction values ​​among the plurality of prediction values ​​and the weighting factors associated with the corresponding prediction models, wherein determining the aggregate prediction value comprises determining a predicted hourly average of the physiological condition associated with the hourly prediction model and a weighted average of corresponding prediction values ​​among a plurality of prediction values ​​associated with one or more different prediction models; and A graphical indication of the aggregate predicted value of the patient's physiological condition associated with a future time is displayed.

12. The method of claim 11, wherein the weighting factors associated with the respective prediction models vary with respect to time.

13. The method of claim 11, wherein determining the reliability metric comprises: determining a forecast range corresponding to a time before the current time in the future day; obtaining historical data associated with the patient for the predicted range, the historical data comprising historical measurement data of a physiological condition associated with the predicted range; and for each corresponding prediction model in the plurality of different prediction models, determining a reference prediction for the prediction horizon based on the historical data using the corresponding prediction model; as well as A reliability metric associated with a corresponding prediction model is determined based on a difference between the reference prediction and the historical measurement data.

14. The method of claim 13, wherein determining the reliability metric comprises determining a mean absolute difference associated with a respective prediction model for the prediction horizon.

15. An electronic device, comprising: a communication interface for receiving current measurement data of the patient's physiological condition from a sensing device; a display device having a graphical user interface display including a graphical representation of said current measurement data; a user interface for obtaining user input for adjusting said graphical user interface display to view future; as well as a control system coupled to the communication interface, the display device, and the user interface for determining, using a first prediction model, a first plurality of predictions of a future physiological condition of the patient based at least in part on current measurement data; determining, using a second prediction model different from the first prediction model, a second plurality of predictions of a future physiological condition of the patient based at least in part on the current measurement data; determining an aggregate prediction of the patient's physiological condition relative to a future time based at least in part on the first plurality of prediction values ​​and the second plurality of prediction values; and displaying a graphical representation of the collective forecast on the graphical user interface display in response to user input; wherein the first plurality of predicted values ​​comprises predicted hourly averages of the patient's physiological condition; Wherein determining the first plurality of predicted values ​​comprises determining a predicted hourly average of the physiological condition using an hourly forecast model associated with the patient.

16. The electronic device according to claim 15, wherein: The ensemble prediction comprises a weighted average of the first plurality of prediction values ​​and the second plurality of prediction values; and Based on a relationship between a first reliability metric associated with the first prediction model and a second reliability metric associated with the second prediction model, weighting factors associated with the respective first prediction model and second prediction model vary with respect to a future time.

17. The electronic device of claim 16, wherein a first one of the weighting factors associated with an hourly forecast model increases as the amount of future time increases, and a second one of the weighting factors associated with a second forecast model decreases as the amount of future time increases.

18. A system comprising: Display devices; a sensing device for obtaining current measurements of the patient's physiological condition; as well as a control system coupled to the display device and the sensing arrangement for determining a plurality of forecast hourly averages of the patient's future physiological condition based at least in part on the current measurement data using an hourly forecast model associated with the patient and displaying the plurality of forecast hourly averages on the display device; Wherein, determining a plurality of predicted hourly average values ​​of the patient's future physiological condition comprises: determining, using a first prediction model, a first plurality of predicted values ​​for a future physiological condition of the patient based at least in part on the current measurement data, wherein determining the first plurality of predicted values ​​comprises determining hourly forecast values ​​for the physiological condition using an hourly forecast model associated with the patient; determining, using a second prediction model different from the first prediction model, a second plurality of predictions of a future physiological condition of the patient based at least in part on the current measurement data; Determine a plurality of predicted hourly averages of the patient's future physiological condition based at least in part on the first plurality of predicted values, the second plurality of predicted values, and weighting factors associated with the respective first and second prediction models, wherein the weighting factors vary relative to future time based on a relationship between a first reliability metric associated with the first prediction model and a second reliability metric associated with the second prediction model.

19. The system of claim 18, wherein the current measurement data comprises current sensor glucose measurement data and the plurality of forecast hourly averages comprises a future plurality of forecast hourly average glucose levels of the patient.

20. The system of claim 19, wherein: The control system predicts one or more of meals, exercise, insulin delivery, and medication dosage at one or more different times in the future based at least in part on historical event log data associated with the patient; and determining the multiple predicted hourly averages includes calculating the multiple predicted hourly average glucose levels based at least in part on the current sensor glucose measurement data and the predicted one or more of meals, exercise, insulin delivery, and medication dosage at one or more different times in the future.

21. The system of claim 18, wherein the hourly forecast model comprises a recurrent neural network comprising a plurality of hourly long short-term memory (LSTM) units.

22. The system of claim 21 , wherein each hourly LSTM cell is configured to output a predicted hourly average glucose level associated with a corresponding hourly interval as a function of one or more events predicted to occur within the corresponding hourly interval and inputs from one or more previous hourly LSTM cells.

23. The system of claim 22, wherein the function is based at least in part on a relationship between respective subsets of the patient's historical measurement data corresponding to respective hourly intervals and respective subsets of the patient's historical event data corresponding to respective hourly intervals.

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