Method and system for encoding insulin dosing rules into a neural-net artificial pancreas (NAP) intended for the computerized treatment of diabetes

Abstract

Embodiments relate to systems and methods for developing and implementing a model. Developing the model can involve receiving an insulin dosing rule (IDR) including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us), generating a saturated data set with the plural Xs and corresponding Us so that the saturated data set has optimal data density, generating a saturated look-up table (STL) with the saturated data set, and encoding the STL into a neural network. Implementing the model can involve receiving a measurement indicative of a metabolic state (Xm), implementing a model a SLT encoded into a neural network, the SLT including a saturated data set of optimal data density, wherein implementation of the model applies the Xm to the SLT to identify a corresponding U, and monitoring, analyzing, or influencing a concentration of glucose levels in a fluid using the identified U.

Description

Attorney Docket No.0087407-000349 METHOD AND SYSTEM FOR ENCODING INSULIN DOSING RULES INTO A NEURAL-NET ARTIFICIAL PANCREAS (NAP) INTENDED FOR THE COMPUTERIZED TREATMENT OF DIABETES CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent application is related to and claims the benefit of priority to U.S. Provisional Patent Application No.63 / 463,232, filed on May 1, 2023, the entire contents being incorporated herein by reference. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] This invention was made with government support under Grant No. DK133148, awarded by the National Institutes of Health. The government has certain rights in the invention. FIELD

[0003] Embodiments relate to systems and methods for developing and implementing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing. BACKGROUND INFORMATION

[0004] Insulin Dosing Rules (IDR).

[0005] Recent advancements in Continuous Glucose Monitoring (CGM) technology have ushered in a new era of automated IDRs. An IDR is a set of rules, defined as a look-up table, a database, a given protocol, or an algorithmic code that utilizes the metabolic state of the patient, X, to automatically generate an optimal insulin dose, U, and regulate a patient's blood glucose levels.

[0006] The metabolic state of the patient, X, is the primary source of information utilized by IDRs to generate new insulin recommendations. This state typically includes the recent history of blood glucose concentrations and insulin injections, while also potentially incorporating additional variables such as meal intake, composition, activity tracking, or insulin sensitivity – – among others. This state is related to the current metabolism of the patient and contains information to generate an insulin recommendation.

[0007] Once the corresponding IDR has been defined, the process of executing it can be understood as an input-output deterministic mapping, U=f(X), that takes the current user state, X, and produces an insulin recommendation. Specifically, the current metabolic state, X, is first measured, and the IDR is processed to generate an insulin recommendation, U=f(X), which is administered to regulate the patient's blood glucose levels.

[0008] Necessity to deploy IDRs in embedded hardware.

[0009] The deployment of IDRs in wearable devices is increasingly recognized as a critical need in the healthcare industry. Manual execution of insulin therapies by the patients has been shown to be prone to errors, miscalculations, and forgetfulness. To ensure the safe and effective execution of the therapies, industry standards advocate for the computation of IDRs within insulin pumps, wearables, or mobile devices. By embedding IDRs in devices that are attached to the user, the therapy can be executed as designed, minimizing the potential for errors and miscalculations.

[0010] However, recent advances in IDR research have led to the development of more complex and precise therapies, which require significant computational power and resources. This presents a challenge as contemporary IDRs are starting to require a prohibitive amount of hardware to be computed, which is not available in wearable devices. As a result, the industry faces a fundamental question of how to implement computationally intensive IDRs within insulin pumps or wearable devices.

[0011] This creates a dichotomy between the need for IDRs to be lightweight, with a low number of operations that can be easily embedded in small devices, and the desire for more advanced IDRs that are more adaptive and precise. This technological challenge is driving the development of innovative solutions that can bridge the gap between these opposing forces, enabling the safe and effective deployment of IDRs in embedded hardware. SUMMARY

[0012] Embodiments relate to a system for developing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing. The system can include a processor. The system can include computer memory having instructions stored thereon that when executed will cause the processor to receive an insulin dosing rule (IDR) including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us). The system can include computer memory having instructions stored thereon that when executed will cause the processor to generate a saturated data set with the plural Xs and corresponding Us so that the saturated data set has optimal data density. The system caninclude computer memory having instructions stored thereon that when executed will cause the processor to generate a saturated look-up table (STL) with the saturated data set. The system can include computer memory having instructions stored thereon that when executed will cause the processor to encode the STL into a neural network.

[0013] Embodiments relate to a system for efficiently processing and applying insulin dosing rules to metabolic data to generate optimized insulin dosing. The system can include a processor. The system can include computer memory having instructions stored thereon that when executed will cause the processor to receive a measurement indicative of a metabolic state (Xm). The system can include computer memory having instructions stored thereon that when executed will cause the processor to implement a model. The model can include a saturated look-up table (SLT) encoded into a neural network, the SLT including a saturated data set of optimal data density, the saturated data set including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us) from an insulin dosing rule (IDR). Implementation of the model can involve applying the Xm to the SLT to identify a corresponding U. The system can include computer memory having instructions stored thereon that when executed will cause the processor to monitor, analyze, or influence a concentration of glucose levels in a fluid using the identified U.

[0014] Embodiments relate to a method for developing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing. The method can involve receiving an insulin dosing rule (IDR) including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us). The method can involve generating a saturated data set with the plural Xs and corresponding Us so that the saturated data set has optimal data density. The method can involve generating a saturated look-up table (STL) with the saturated data set. The method can involve encoding the STL into a neural network.

[0015] Embodiments relate to a method for efficiently processing and applying insulin dosing rules to metabolic data to generate optimized insulin dosing. The method can involve receiving a measurement indicative of a metabolic state (Xm). The method can involve implementing a model. The model can include a saturated look-up table (SLT) encoded into a neural network, the SLT including a saturated data set of optimal data density, the saturated data set including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us) from an insulin dosing rule (IDR). Implementation of the model can involve applying the Xm to the SLT to identify a corresponding U. The method can involve monitoring, analyzing, or influencing a concentration of glucose levels in a fluid using the identified U.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features and advantages of the present disclosure will become more apparent upon reading the following detailed description in conjunction with the accompanying drawings, wherein like elements are designated by like numerals, and wherein:

[0017] FIG.1A shows an exemplary system for developing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing;

[0018] FIG.1B shows an exemplary system for efficiently processing and applying insulin dosing rules to metabolic data to generate optimized insulin dosing;

[0019] FIG.2A is an exemplary illustration of an IDR expressed as a protocol;

[0020] FIG.2B is an exemplary illustration of an embodiment of the NAP;

[0021] FIGS.3A, 3B, 3C, and 3D illustrate a higher density region and a lower density region;

[0022] FIGS.4A and 4B show deep Learning on Saturated Data Set and Conversion to Residual Neural Network;

[0023] FIG.5 illustrates how robustness of the model can be measured;

[0024] FIG.6 illustrates how any insulin dosing rule, including automated insulin delivery (AID) algorithms, can be reproduced by the neural network;

[0025] FIG.7 shows an exemplary system implementation;

[0026] FIG.8 is an exemplary high-level functional block diagram for an embodiment of the present invention, or an aspect of an embodiment of the present invention;

[0027] FIG.9 shows an exemplary computing device configuration;

[0028] FIG.10 is a block diagram that illustrates an exemplary system including a computer system and the associated Internet connection upon which an embodiment may be implemented;

[0029] FIG.11 illustrates a system in which one or more embodiments of the invention can be implemented using a network, or portions of a network or computers; and

[0030] FIG.12 is a block diagram illustrating an example of a machine upon which one or more aspects of embodiments of the present invention can be implemented. DETAILED DESCRIPTION

[0031] Embodiments disclosed herein improve operation of one or more processors 102. This can be achieved by implementing the disclosed model that will improve efficiency of processor 102 operation by requiring less iterations, requiring less computational resources,etc. For instance, conventional techniques rely on processing insulin dosing rules (IDRs) in raw form, which requires significant computational resources and prohibits such processing being done on hardware-limited platforms (e.g., wearable devices). The model, however, generates a saturated data set with optimal data density to allow for generation of a saturated look-up table (SLT) – i.e., it structures a database of data in a novel and non-conventional way to provide for a specific purpose look-up table suitable for insulin dosing. This specific purpose look-up table can then be encoded into a neural network. A processor 102 implementing the model can receive a metabolic state (which may include one or more complex variables) of a patient and very quickly and efficiently find an insulin dosage recommendation via the encoded STL. In other words, the processor 102 can generate a same or even more accurate and precise result with less computational resources (e.g., requiring less iterations, requiring less processor size and components, etc.) than otherwise would be achievable, thereby allowing for implementation on hardware-limited platforms.

[0032] Referring to FIGS.1A and 1B, embodiments can relate to a system 100 for developing and / or implementing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing. The system 100 can include a processor 102. The system 100 can include memory 104, which can include computer memory. The memory 104 can be associated with the processor 102. The memory 104 can have instructions 106 stored thereon that when executed will cause the processor 102 to execute algorithmic steps for developing and / or implementing embodiments of the model.

[0033] It should be noted that the system 100 or processor 102 for developing a model can be the same system 100 or processor 102 or a different system 100 or processor 102 than the system 100 or processor 102 used for implementing the model.

[0034] The processor 102 can be any of the processors 102 disclosed herein. The processor 102 can be part of or in communication with a machine (logic, one or more components, circuits (e.g., modules), or mechanisms). The processor 102 can be hardware (e.g., processor, integrated circuit, central processing unit, microprocessor, core processor, computer device, etc.), firmware, software, etc. configured to perform operations by execution of instructions embodied in algorithms, data processing program logic, artificial intelligence programming, automated reasoning programming, etc. It should be noted that use of processors 102 herein can include any one or combination of a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), etc. The processor 102 can include one or more processing modules. A processing module can be a software or firmware operating module configured to implement any of the method steps disclosed herein.The processing module can be embodied as software and stored in memory 104, the memory 104 being operatively associated with the processor 102. A processing module can be embodied as a web application, a desktop application, a console application, etc. Exemplary embodiments of the processor 102 and the machine are discussed later.

[0035] The processor 102 can include or be associated with a computer or machine readable medium. As discussed in more detail later, the computer or machine readable medium can include memory 104. The computer or machine readable medium can be configured to store one or more instructions 106 thereon. The instructions 106 can be in the form of algorithms, program logic, etc. that cause the processor 102 to build and implement embodiment of the model.

[0036] Any of the memory 104 discussed herein can be computer readable memory configured to store data. The memory 104 can include a volatile or non-volatile, transitory or non-transitory memory, and be embodied as an in-memory, an active memory, a cloud memory, etc. Embodiments of the memory 104 can include a processor module and other circuitry to allow for the transfer of data to and from the memory 104, which can include to and from other components of a communication system. This transfer can be via hardwire or wireless transmission. The communication system can include transceivers, which can be used in combination with switches, receivers, transmitters, routers, gateways, wave-guides, etc. to facilitate communications via a communication approach or protocol for controlled and coordinated signal transmission and processing to any other component or combination of components of the communication system. The transmission can be via a communication link. The communication link can be electronic-based, optical-based, opto-electronic-based, quantum-based, etc.

[0037] The processor 102 can be in communication with other processors of other devices 108 (e.g., a glycemic state monitoring device, a glucose management system, an insulin recommendation system, an insulin delivery device, a non-portable computer device, a portable computer device, a hand-held computer device, a wearable computer device, a smartphone, a smartwatch, etc.). Any of those other devices 108 can include any of the exemplary processors disclosed herein. Any of the processors can have transceivers or other communication devices / circuitry to facilitate transmission and reception of wireless signals. Any of the processors can include an Application Programming Interface (API) as a software intermediary that allows two applications to talk to each other. Use of an API can allow software of the processor 102 of the system 100 to communicate with software of the processorof the other device(s) 108, if the processor 102 of the system 100 is not the same processor of the device 108.

[0038] Any data transmission between the processor 102 and memory 104, between the processor 102 and a database 110, and between the processor 102 and processors of other devices 108 can be via a pull operation (e.g., the processor 102 can pull the data) or a push operation (e.g., the data can be pushed to the processor 102). The processor 102 can receive the data in steaming format, or store it in memory 104 before being processed. In addition, embodiments of the model disclosed herein can be developed an application software (an “App”) to be implemented on a processor of a device 108 (e.g., a wearable device). The App can be sent via a steaming format, or the App can be sent and stored on a memory associated with or accessed by the device 108.

[0039] As noted herein, the processor 102 can be configured to be a component of, used in combination with, or in communication with another device / system 108 – e.g., this can include the processor 102 being part of the device / system 108, the device / system 108 being part of the processor 102, the processor 102 in communication with the device / system 108, etc. “Being part of” can include being on a same substrate or integrated circuit. For instance, the processor 102 can be a component of, used in combination with, or in communication with a predictive modeling system (e.g., a system for predicting risk of hypo- or hyper-glycemia), a decision support system (e.g., a system for assisting with medical triage), an automated control system (e.g., an artificial pancreas), etc. The processor 102 can use the model or provide the model to the device / system 108 to assist with or augment the performance of these devices / systems 108. Output of the model can be used by the device / system 100 to assist with or augment predicting or reacting to aspects of glycemic states, assist with or augment determining or modifying insulin administration therapies, etc.

[0040] Embodiments can relate to one or more systems 100 for developing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing. The system 100 can include one or more processors 102. The system 100 can include one or more computer memories 104 having instructions 106 stored thereon that when executed will cause the processor 102 to execute one or more functions disclosed herein.

[0041] The instructions 106 can cause the processor 102 to receive one or more insulin dosing rules (IDRs) including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us). For instance, an IDR can be a set of rules that an individual with type- 1 or type-2 diabetes must adhere for self-blood glucose regulation. In a computerized system, an IDR can be configured as a protocol, a look-up table, a database, an algorithm, a formula,code function, etc. For instance, an IDR can be a fixed mapping of U=f(X). Thus, the IDR can be a data structure stored in memory 104 or a database 110 – i.e., the processor 102 can receive the IDR from the computer memory 104 or from a data store (e.g., a database 110). A system utilizing the IDR can receive a measurement(s) representative of a metabolic state (e.g., a glucose measurement, weight, BMI, meal intake, exercise, etc.) from a meter (e.g., glucose measuring or monitoring device, activity sensor, etc.), and find a corresponding insulin dosage recommendation within the IDR to generate an optimal insulin dosage amount or insulin dosage rate. Finding the corresponding insulin dosage recommendation can be finding an insulin dosage recommendation that closely matches with, correlates to, is determined to be the most optimal, etc., which can be determined via interpolation, regression, use of a cost function, use of a quadratic formula, use of a runtime lookup logic for an array, etc.

[0042] It is contemplated for there to be one IDR for each individual patient (or user) of the system 100, but there may be more than one IDR for an individual patient. It is further contemplated for there to be an individual system 100, or device 108 (e.g., wearable device), or model for each individual patient, but there may be more than one system 100 / device 108 / model for an induvial patient and there may be more than one patient for an individual system 100 / device 108 / model.

[0043] The processor 102 may receive the entire IDR or a portion thereof.

[0044] The instructions 106 can cause the processor 102 to generate a saturated data set with the plural Xs and corresponding Us of the IDR. Generating a saturated data set can involve data processing operations that will result in a data set having optimal data density. This can be a uniform data density, for example For instance, the IDR can include plural Xs and corresponding Us, but when presented on a graph there may be regions of lower density (e.g., less Xs and corresponding Us) than other regions – this would be non-optimal data density. Tests for optimal data density can include a Kolmogorov-Smirnov test, a chi-square test, Nearest neighbor Graph, etc. Should there exist non-optimal density in the data set, Xs and corresponding Us can be generated and included with the saturated data set in order to provide for an optimal data density. Quantitative measures for determining a threshold level of uniformity (e.g., at what point is the data uniform (for example) enough to be optimal) can be derived by looking at how much ‘Us’ changes as we move from one element in the dataset to its closest neighbors. Optimal density can be achieved when, for every element in the data set, we can verify that, at least, its ’N’ closest neighbors provide an insulin recommendation, ‘Us_neighbor’, that does not differ from ‘Us’ in more than a predefined tolerance, i.e. a minimum safety margin of allowed deviation. If this condition is not met for some elements inthe dataset, there may be located in non-optimal regions that require further population. If condition is met, current density is enough. Additional understanding regarding optimal density and discussions on constructing algorithms to measure the same can be appreciated from the following reference, the entire contents of which is incorporated herein by reference. - Neural Networks for on-chip Model Predictive Control: a new method to build Saturated Optimal-Density Training Datasets by A. Castilloa, E. Pryora, A. El Fathia, B. Kovatcheva, M. Bretonaa. Center for Diabetes Technology, University of Virginia, US. IFAC Journal of Systems and Control. April 29, 2024.

[0045] This can be done via a simulation technique, a predictive technique, an interpolation technique, a regression technique, a nearest neighbor technique (e.g., a nearest neighbor graph technique), etc. For instance, the processor 102 can receive a measurement indicative of a metabolic state (Xm) from a glucose monitoring device or a glucose measurement device. The processor 102 can then identify a corresponding U or even determine a new U using a simulation technique, a predictive technique, an interpolation technique, a regression technique, a nearest neighbor technique (e.g., a nearest neighbor graph technique), etc. The processor 102 can then include the Xm and corresponding U with the saturated data set to populate a lower density region. This process can continue until a desired optimal data density is obtained. The saturated data set can then be stored as a data structure in memory 104.

[0046] The instructions 106 can cause the processor 102 to generate one or more saturated look-up tables (STLs) with the saturated data set. This can involve generating one or more arrays with the saturated data set such that a runtime lookup logic for the array(s) can be used to find a U for a given X. The STL set can be stored as a data structure in memory 104.

[0047] The instructions 106 can cause the processor 102 to encode the STL into a neural network (e.g., a kernel neural network). The encoding can be via an integer encoding technique, a one hot encoding technique, a learned embedded technique, etc. With the specific purpose look-up table encoded into the neural network, a processor implementing the model can receive a metabolic state X (which may include one or more complex variables) of a patient and very quickly and efficiently find an insulin dosage recommendation U via the encoded STL. Conventional systems require processors to apply the metabolic state X to an IDR, which is a very taxing of computational resources for a processor.

[0048] The STL encoded into the neural network can be the model. It is contemplated for the model to be developed an application software (an “App”) to be implemented on a processor of a device 108 (e.g., a smartwatch). Thus, the model can be used by the device 108 to very quickly and efficiently find an insulin dosage recommendation U when receiving a metabolic state measurement X. The App can be sent via a steaming format, or the App can be sent and stored on a memory associated or accessed by the device 108.

[0049] The instructions 106 can cause the processor 102 to train the neural network with a cost function. Details of the cos function are provided below.

[0050] In some embodiment, instructions 106 can cause the processor 102 to a hypoglycemia protection rule and / or a bolus priming rule into the STL.

[0051] With the hypoglycemia protection rule, the goal is to mitigate / avoid hypoglycemia situations. Hypoglycemia can be a condition where blood glucose levels drop below 70 mg / dL, for example. A trigger for hypoglycemia can be excessive insulin administration or an increased physical activity without appropriate adjustments to food or insulin intake. Any recommended insulin infusion should not be administered if there is imminent risk of hypoglycemia. The hypoglycemia protection rule can be program logic, a decisioning mechanism, etc. to reduce, attenuate, suspend, etc. an insulin dose amount or rate based on an associated risk of hypoglycemia. The risk can be assessed using current glucose level and a history of insulin deliveries, wherein a maximum allowable upper bound for insulin infusions is determined based on the observed risk of hypoglycemia. The observable risk can be a trendline, a regression analysis, etc. in which the blood glucose level will drop below 70 mg / dL if an insulin dosage amount or rate continues as anticipated. The upper bound can range from zero (insulin infusions are suspended due to a severe risk of hypoglycemia) to infinity (insulin doses are not reduced or suspended due to a lack of hypoglycemia risk). The upper bound can be encoded into the STL.

[0052] With the bolus priming rule (BPR), the goal is to mitigate / avoid hyperglycemia situations. Hyperglycemia can be defined as having blood glucose concentrations higher than 180 mg / dL, for example. While hyperglycemia may not pose an immediate risk of harm, prolonged periods of high glucose concentration can lead to long-term health complications. Hyperglycemia typically arises from either carbohydrate-rich meal ingestion or insufficient insulin in the user's bloodstream for a certain amount of time.

[0053] The BPR can be program logic, a decisioning mechanism, etc. to monitor the user's glucose levels and a history of insulin deliveries. With this information, two probabilities can be computed: (i) the probability of a meal occurring right now; and (ii) the probability of beingin hyperglycemia right now and remain there during the next hour. This can be done using predictive analytics, etc. If any of both probabilities are higher than a threshold, the BPR can generate a recommendation or command signal to infuse an insulin bolus that will avoid or reduce the hyperglycemia. This probability assessment can be encoded into the STL.

[0054] Embodiments can relate to a system 100 for efficiently processing and applying insulin dosing rules to metabolic data to generate optimized insulin dosing. The system 100 can include a processor and computer memory 104 having instructions 106 stored thereon that when executed will cause the processor to implement the model. Thus, the system 100 can be a device 108 discussed above. It is contemplated for the device 108 to be a wearable device. The instructions 106 can cause the processor 102 to receive a measurement indicative of a metabolic state (Xm). This can be from a glucose monitoring device, a glucose measuring device, an activity monitoring device, etc. In some embodiments, the device 108 itself can provide the Xm. The instructions 106 can cause the processor 102 to implement the model. As noted herein, the model can include a saturated look-up table (SLT) encoded into a neural network. The SLT can include a saturated data set of optimal data density, wherein the saturated data set includes plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us) from an insulin dosing rule (IDR). Implementation of the model can involve applying the Xm to the SLT to identify a corresponding U (e.g., using a simulation technique, a predictive technique, an interpolation technique, a regression technique, a nearest neighbor technique, etc.). The instructions 106 can cause the processor 102 to monitor, analyze, or influence a concentration of glucose levels in a fluid using the identified U.

[0055] The instructions 106 can cause the processor 102 to receive the model as a streaming software application, the model can stored on the computer memory 104 as a software application and the instructions 106 can cause the processor 102 to retrieve the model therefrom, or the model can be stored in a data store as a software application and the instructions 106 cause the processor 102 to retrieve the model therefrom.

[0056] The processor 102 can be configured to be a component of, used in combination with, or in communication with a predictive modeling system, a decision support system, an automated control system, a non-portable computer device, a portable computer device, a hand-held computer device, a wearable computer device, an insulin monitoring device, an insulin delivery device, etc. For instance, the processor 102 can be configured to be a component of, used in combination with, or in communication with an insulin delivery device 108, wherein the instructions 106 can cause the processor 102 to generate a command signalconfigured to cause the insulin delivery device to administer or adjust an insulin dosage amount or an insulin dosage rate based on the identified U.

[0057] Embodiments can relate to a method for developing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing. The method can involve receiving an insulin dosing rule (IDR) including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us). The method can involve generating a saturated data set with the plural Xs and corresponding Us so that the saturated data set has optimal data density. The method can involve generating a saturated look-up table (STL) with the saturated data set. The method can involve encoding the STL into a neural network.

[0058] Embodiments can relate to a method for efficiently processing and applying insulin dosing rules to metabolic data to generate optimized insulin dosing. The method can involve receiving a measurement indicative of a metabolic state (Xm). The method can involve implementing a model. The model can included a saturated look-up table (SLT) encoded into a neural network. The SLT can include a saturated data set of optimal data density. The saturated data set can include plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us) from an insulin dosing rule (IDR). Implementation of the model can involve applying the Xm to the SLT to identify a corresponding U. The method can involve monitoring, analyzing, or influencing a concentration of glucose levels in a fluid using the identified U.

[0059] EXAMPLES

[0060] The following are exemplary systems, methods, and implementations of the embodiments disclosed herein. While the examples may focus on one implementation, it is understood that this is exemplary and the embodiments disclosed herein are not limited thereto.

[0061] Embodiments of the model or system 100 can be referred to as a Neural Artificial Pancreas (NAP). An exemplary NAP can be a method that enables the translation of Insulin Dosing Rule (IDR), or a plurality of IDRs, into a neural network representation that is suited for execution on hardware-limited platforms, such as insulin pumps or wearables devices, for the treatment of diabetes and related insulin-requiring disorders. Additionally, it has the potential to incorporate hyperglycemia / hypoglycemia protection mechanisms to enhance its safety.

[0062] An IDR is defined as a set of rules that an individual with type-1 or type-2 diabetes must adhere for self-blood glucose regulation. Typically, IDRs are presented as a protocol, a look-up table, a database, an algorithm, a formula, or code function. They can be seen as an automated decision process that takes certain metabolic variables of the subject –denoted as X–as source information and automatically generate an optimal insulin dose –denoted as U– associated with that metabolic state. The generated insulin dose is injected to maintain glycemic control.

[0063] Embodiments of the NAP method can serve three main purposes: (i) implement and reproduce a set of IDRs; (ii) enhance said IDRs by incorporating Hyperglycemia / Hypoglycemia Protection (HHP) mechanisms, and / or (iii) enhance said IDRs by incorporating a Bolus Priming Rule (BPR).

[0064] The NAP method can comprise 4 key elements: (1) the Nearest Neighbor Graph (NNG), which is an algorithm that processes IDRs to assess its completeness and produces a Saturated Look-up Table (SLT) –– a standardized representation of the IDRs. (2) the Kernel Neural Net (KNN), which is an adaptable neural network capable of effectively learning and replicating an insulin therapy; (3) the HHP mechanisms, which are elements designed to improve protection against both hyperglycemia and hypoglycemia situations, and (4) the BPR, which reduces patient-system interaction typically associated with meals.

[0065] An application of the NAP method can be into the design and manufacturing of Automated Insulin Delivery (AID) systems. Specifically, it can provide a safe and viable deployment option for insulin therapies, which are becoming increasingly complex and difficult to automate and implement in wearable devices. The NAP method can represent a novel approach to address these challenges and pave the way for greater automation and safety in diabetes management.

[0066] An aspect of an embodiment of the present invention generally relates to, but not limited thereto, medicine and medical devices, as used for insulin treatment of diabetes mellitus and other metabolic disorders, including but not limited to type 1 and type 2 diabetes, type 2 (T1D, T2D), latent autoimmune diabetes in adults (LADA), postprandial or reactive hyperglycemia, insulin resistance, etc. In alternative embodiments, the invention can include (i) a method for encoding insulin dosing rules into a Neural-net Artificial Pancreas (NAP) intended for the computerized treatment of diabetes; (ii) Hyperglycemia / Hypoglycemia Protection mechanisms, and / or (iii) a Bolus Priming Rule (BPR). One of the major advantages (among others) of an aspect of an embodiment of the present invention is the ability to, but not limited thereto, encode insulin dosing rules, into a neural network representation that is suited for execution on hardware-limited platforms, such as insulin pumps or wearables devices. Another advantage is that, in different embodiments of NAP can encode insulin dosing rules formulated via protocol, a look-up table, a database, an algorithm, a formula, code function, etc. Applications of embodiments of the present invention may include, but are not limited to,automated delivery of insulin and other drugs in people with type-1 or type-2 diabetes, as well as diabetes decision support systems.

[0067] Defining the Neural Artificial Pancreas (NAP).

[0068] NAP can be a method that enables the translation of a given IDR, or set of IDRs, into a neural network representation that is suited for execution on hardware-limited platforms. Moreover, the NAP can integrate Hyperglycemia and Hypoglycemia Protection (HHP) modules to bolster safety and a Bolus Priming Rule (BPR) module to improve the IDR behavior in response to meals.

[0069] The NAP can encompass four key features: I. A Nearest Neighbor Graph (NNG), which processes IDRs defined via protocol, a database, an algorithm, a formula, or code function, in order to produce a Saturated Look-up Table (SLT) –– a standardized representation of the IDRs. The SLT covers comprehensively all the considered scenarios in the IDR with sufficient granularity and detail. II. A Kernel Neural Network (KNN), which is a procedure for encoding an IDR, or a set of IDRs, pre-processed by the NNG into a neural network representation that accurately reproduces them and is better suited for execution in hardware-limited platforms. III. Optional Safety Rules that augment the safety of the provided IDR by incorporating hypoglycemia protection (Hypo-P) mechanisms, thereby enhancing the system's reliability, and / or IV. An optional Bolus Priming Rule (BPR), which recommends insulin boluses to be delivered according to the confidence in upcoming meals or prevailing risk or hyperglycemia.

[0070] An application of the NAP method can be Automated Insulin Delivery (AID) – e.g., the system 100 or device 108 can be an AID. It is conceived to create a feasible and safe deployment path for modern insulin therapies.

[0071] It may be particularly useful whether the target therapy is computationally intensive and cannot be directly deployed in wearable devices.

[0072] FIG.2A is an exemplary illustration of an IDR expressed as a protocol. In the left panel, patients have been provided with a set of rules to execute for manual self-blood glucose regulation, they measure a given metabolic variable, then they calculate the associated insulin and infuse it. In the middle panel, an IDR is given as an algorithm or piece of code. The insulin regulation is automatically done by the algorithm, which receives the measurements and performs the calculations. In the right panel, an IDR is translated to the NAP. The NAPencodes the IDR, given either as a protocol or an algorithm, in a lightweight neural network that is better suited for execution in hardware-limited platforms.

[0073] FIG.2B is an exemplary illustration of an embodiment of the NAP. It is intended to create a feasible and safe deployment path for modern insulin therapies. The IDR is encoded in a neural network, which is better suited for execution in hardware-limited platforms. The added BPR and Hypo-P modules integrate hyperglycemia / hypoglycemia protection mechanisms to bolster safety. These modules modify the insulin recommendations of the IDR in situations where there is a predicted risk of hyperglycemia or hypoglycemia. They can be run after the neural network –left panel– or they can encoded into the neural network –– right panel.

[0074] The NAP method.

[0075] Embodiments of the NAP method can be referred to as a process of receiving a given IDR and producing a neural network that implements it with optional safety modules for hypoglycemia or hyperglycemia protection (HHP) and a bolus priming rule (BPR) to improve its behavior in response to meals.

[0076] In this section we detail this process and exemplary steps to execute it, which can be: 1. Express the IDR in a standardized format –a Saturated Look-up Table (SLT)– with a Nearest Neighbor Graph (NNG). 2. Encode the SLT into a Kernel Neural Network (KNN). 3. Measure the robustness of the KNN and its ability to interpolate between elements in the SLT. 4. Add optional modules for hypoglycemia protection and hyperglycemia mitigation.

[0077] Step 1: Express the IDR in a standardized format –a Saturated Look-up Table (SLT)– with a Nearest Neighbor Graph (NNG).

[0078] As previously noted, IDRs all function in a similar manner: they measure a user's metabolic state, X, which is a set of variables, and then generate an insulin recommendation, U, that is dependent on X. Thus, an IDR can be simply understood as a fixed mapping, U=f(X), from inputs to outputs. Given the same input, the IDR will always recommend the same insulin infusion.

[0079] The IDR mapping can be presented in various formats, with the most common being as a set of rules or protocols, as a dataset, as an algorithm or as a mathematical equation. In order to NAP a given IDR, the first step is to express the IDR in a standardized format, –i.e., a Saturated Look-up Table (SLT)– by means of a Nearest Neighbor Graph (NNG).

[0080] The SLT is a standardized format for representing IDRs. (See Table 1). Protocols, datasets, algorithms, or mathematical equations can all be standardized as a SLT. The SLT is constructed by sampling metabolic states, X, and generating insulin recommendations, U, based on the original IDR. At the end, we will have a look-up table that relates states, X, to their associated insulin infusions, U. Thus, instructions 106 can cause the processor 102 to iterate through the U=f(X) of the IDR and generate the SLT in Table 1. Table 1: Standardized Saturated Look-up Table (SLT) for an IDR Recorded Metabolic States Associated Insulin Doses X1U1

[0081] e s a e c e a go a u s a ea es e g o g ap w e elements of the SLT. This graph is used for: (i) efficiently searching and retrieving elements from the SLT, (ii) measuring different properties of the SLT, such as its connectivity, the proximity between elements, its density, the presence of isolated points, or the existence of low or extra populated regions; and (iii) populating the SLT with new elements, {X,U}, that are specifically selected to improve the properties of point (ii), ultimately enhancing the overall accuracy of the IDR.

[0082] Once the SLT has been built, it will contain all the needed information to execute the therapy. The SLT will operate as follows: (i) a new user state, Xu, is first measured; (ii) the new state Xu is queried to the NNG, which will look for and find its closest neighbor in the SLT, Xn,u; (iii) the insulin recommendation corresponding to the found closest state Un,uis returned.

[0083] Populating the SLT with the NNG.

[0084] The SLT requires a deliberate population process to ensure its density is sufficient and optimal, devoid of isolated points or lower-density regions for accurate IDR representation. As illustrated in FIGS.3A-3D, a well-defined SLT features uniformly distributed inner states, Xk, that are all sufficiently close to each other. FIGS.3A-3D illustrate a higher density region and a lower density region. Each blue point represents a metabolic state, Xk, of the SLT, which is aset of numerical variables. The orange states can be discarded to reach optimal density. These FIGS. also illustrate a sufficiently dense SLT and a low density region. When a new user’s metabolic state, X^, is measured, the NNG finds its closest neighbor, X୬,^, in the SLT and returns its associated insulin recommendation. These FIGS. also illustrate the Nearest Neighbor Graph and how it is used to detect low-density regions or isolated elements. A low density region will have longer connections to their neighbors, being therefore detectable. The NNG detects these areas and better populates them to reach the same level of density.

[0085] This property is crucial for accurately approximating a new user's state, Xu, by its nearest neighbor, Xn,u. Failing to maintain this property may result in the SLT's inability to represent the original IDR.

[0086] Thus, the SLT is generated and refined with the NNG in an iterative process, that includes the following main steps: 1. Define a desired level of density, ^^, for the SLT. 2. Remove duplicate elements –– i.e. states that fall closer than ^^ to others (orange dots in FIGS.3A-3D). 3. Get local density measurements through the graph connections (see FIG. 3A-3D) and identify areas that need more population –– i.e. areas whose local density measure is lower than ^^. 4. Artificially sample states in to better populate these areas. After this process, we produce an SLT that accurately reflects the original IDR with ^^-discretization scale.

[0087] Step 2: Encode the SLT into a Kernel Neural Network (KNN).

[0088] Once the look-up table has been built, it is encoded into a neural network.

[0089] The SLT with its NNG cannot directly serve as an IDR implementation as its size can be immense, sometimes reaching terabytes, which makes it impossible to deploy in wearable devices. Additionally, the algorithm or the set of rules that were originally used to generate it can be computationally intensive and similarly impossible to deploy in wearable devices. The NAP method's ultimate goal is to encode this SLT into a lightweight neural network (e.g., a neural network model having smaller size and lower computational requirements), which is specifically suited for execution on hardware-limited platforms. A neural network model can be made “lightweight” by using model scaling, neural architecture searching, group convolution, squeeze & excitation, mobile transformation, etc. techniques.

[0090] Our current research illustrates that the KNN can encode in less than 30kB of parameters an SLT that occupies around 1.000Gb of memory and has more than 1 billion rows.This capacity of compressing the information is the main ‘power’ of the NAP method that makes it suitable as a deployment strategy.

[0091] Kernel Neural Network Architecture (KNN).

[0092] The Kernel Neural Network is illustrated in FIGS.4A and 4B. It has been optimized to require a low memory footprint and a reduced number of operations, being also easy to implement from scratch in an embedded platform.

[0093] The neural network is based on a Residual NETwork (ResNET) architecture, consisting of a stack of N ResNET blocks (orange boxes). Each block receives an input vector, ^^^, and generates an output vector, ^^^ା^, through a series of operations, including normalization, scaling, and affine transformations, as well as fully connected neural network layers with Rectified Linear Unit activation functions (ReLU layers).

[0094] The operations on the input data that each block performs are listed as follows: ^^ì ^^^,^ൌ ^ െ ^^^ ^^ ^ ^^^^^ ^^ ^^^ ^ ^^^^^where ^^ ൌ ^1, … , ^^^ denotes the input usermetabolic state, and ^^^,^, ^^^,ଶ, … , ^^^,ெare internal vectors.

[0095] The ResNET can adapt its internal parameters –i.e. ^^^, ^^^, ^^^, ^^^, ^^ ^^ ^^^, ^^ and ^^– to produce the same insulin recommendation, ^^^, as the look-up table for all the input states ^^^. Hence, the look up table can be encoded inside these parameters, which is significantly more efficient than other compression methods.

[0096] Finally, the matrix ^^^is set as the identity if the input-output block dimensionalities are equal, or it is set as a learnable parameter otherwise. The final output of the network is generated by an affine transformation ^^^^ൌ ^^ ^^ேା^^ ^^, with A and b also being adjustable parameters. The network output is then passed through a saturation function to ensure that it satisfies certain thresholds, such as positive insulin doses and maximum allowable doses.

[0097] Neural Network Training.

[0098] In this subsection, we discuss the process of training the neural network, including the cost definition, training techniques, and frameworks that can be used. We also outline the primary metrics used to assess performance after training.

[0099] The neural network is trained to minimize the following cost function: ே ^^ ൌ ^^ ^^ ^^ ^^ ൌ^^ ^^^െ ^^^ேே^ଶwhere ^^^is the target insulin and ^^^ேேis the recommendation generated by the neural

[0100] It is trained using a machine learning framework, such as PyTorch or TensorFlow. The training process involves adjusting the network's parameters –i.e. ^^^, ^^^, ^^^, ^^^, ^^ ^^ ^^^, ^^ and ^^– in order to minimize the above cost function. To this end, the training data set, consisting of all input-output pairs {Xk, Uk} in the SLT, is divided into mini-batches, and the gradient of the cost function with respect to each adjustable parameter is computed for each mini batch. Parameters are updated iteratively using gradient descent steps until the desired level of accuracy is reached.

[0101] Evaluation Metrics.

[0102] The neural network's primary accuracy is assessed by comparing its insulin recommendations with the ones that the original SLT will recommend. The insulin recommendation error is computed for each element in the SLT and several metrics are computed to evaluate it, including: ^ 99.9therror percentile. ^ Average absolute error. ^ Average error. ^ Maximum errors. ^ Error’s distribution across different metabolic variables.

[0103] To ensure that the neural network has accurately encoded the SLT, we impose maximum tolerable errors. The 99.9th percentile error should be within 0.25U of insulin –– indicating that the 99.9% of points in the SLT produce an error that is lower than 0.25U of insulin. The mean error should be lower than 0.25U / h of insulin. Additionally, the maximum error in 'safe' situations –i.e., high blood glucose values where there is no immediate risk of hypoglycemia– should not exceed 0.75U. The maximum errors in 'critical' situations –i.e., imminent risk of hypoglycemia– should be less than 0.1U / h.

[0104] If these error metrics are satisfied, it can be concluded that the KNN has effectively encoded the SLT and therefore it can effectively retrieve and insulin doses, Uk, in a reasonably equal way than the original SLT.

[0105] Step 3: Measure the robustness of the KNN and its ability to interpolate between elements.

[0106] After encoding the SLT into the KNN, it can be desirous to assess its robustness –– i.e. its ability to accurately interpolate between elements in the table. This evaluation may be necessary to ensure that the KNN can also generate insulin recommendations for states, X_u, that fall in between other ones. The SLT requires a deliberate population process to ensure its density is sufficient and optimal, devoid of isolated points or lower-density regions for accurate IDR representation. A well-defined SLT features uniformly distributed inner states, Xk, that are all sufficiently close to each other.

[0107] Referring to FIG.5, this robustness is measured through two mechanisms: 1. With a test dataset deliberately generated with the NNG To evaluate the KNN's robustness, a validation dataset consisting of metabolic states and their associated insulin recommendations is deliberately generated with the NNG. The NNG is used to sample states, X, that fall in between states that already exist in the SLT. We can generate as many testing points as desired in order to get an accurate notion about the network’s robustness. The same performance metrics can be computed to quantify the accuracy and reliability of the KNN's interpolations as well. If the performance metrics meet the above criteria, the KNN can be considered a robust representation of the IDR. Otherwise, the neural network's design or SLT density ^^ should be revisited and modified. 2. With an adversarial agent A second method to assess the KNN's robustness is by using an adversarial agent. An adversarial agent generates synthetic metabolic states, ^^^,^ௗ௩, that are deliberately chosen in order to maximize the KNN’s error. These states are crafted to fall within a distance lower than ^^ with respect to the SLT ones, ^^^, in the SLT –– i.e. they will fall inside the circles of radius ^^. The SLT requires a deliberate population process to ensure its density is sufficient and optimal, devoid of isolated points or lower-density regions for accurate IDR representation.

[0108] The adversarial inputs are generated by maximizing a cost over a domain ^^^ൌ^ ^^:|^^|^|^^^ ^ ^^|^:^^^,^ௗ௩ൌ arg ∀ m ^⊂a ^x ^^ ^^^ ^^^ െ ^^^ேே^ ^^^^ଶ^

[0109] In this way, forwill look for an ^^- neighbor that maximizes the KNN error. It is required that these maximal error states alsomeet the above criteria in terms of allowable errors. The SLT requires a deliberate population process to ensure its density is sufficient and optimal, devoid of isolated points or lower- density regions for accurate IDR representation.

[0110] Step 4: Add optional modules for hypoglycemia protection and hyperglycemia mitigation.

[0111] Referring to FIGS.6 and 7, the final step of the NAP method involves adding safety modules in order to enhance safety against hypoglycemia (low blood sugar), prevailing hyperglycemia (high blood sugar during more than 1h), and meal intakes (anticipated hyperglycemia due to carbohydrates intakes). These modules are the Bolus Priming Rule (BPR) and the Hypoglycemia Protection (Hypo-P).

[0112] The Bolus Priming Rule (BPR).

[0113] Its target is to mitigate / avoid hyperglycemia situations.

[0114] Hyperglycemia is defined as having blood glucose concentrations higher than 180 mg / dL. Hyperglycemia does not pose an immediate risk, however, prolonged periods on high glucose concentrations can lead to long-term health complications. Hyperglycemia typically arises from either carbohydrate-rich meal ingestion or insufficient insulin in the user's bloodstream for a certain amount of time.

[0115] The BPR is intended to mitigate / avoid hyperglycemia. This module operates by continuously monitoring the user's blood glucose levels, ^^, and the recent history of insulin deliveries, ^^. With this information, the module computes two probabilities: (i) the probability of a meal occurring right now; and (ii) the probability of being in hyperglycemia right now and remain there during the next hour. If any of both probabilities are higher than a threshold, the BPR automatically infuses insulin bolus, ^^ ^^^^ ^^, ^^^, intended to avoid or reduce the hyperglycemia.

[0116] The main job of the BPR is just to indicate when an insulin bolus should be injected due to a predicted risk of hyperglycemia. However, it will not decide by itself how much insulin is in fact needed in that bolus. The amount of insulin should be pre-configured by the user according to his needs and his related IDR.

[0117] The Hypoglycemia Protection module (Hypo-P).

[0118] Its target is to avoid hypoglycemia situations.

[0119] Hypoglycemia is a condition where blood glucose levels drop below 70 mg / dL, and it can indeed become life-threatening if it is not promptly addressed. The trigger for hypoglycemia is excessive insulin administration or an increased physical activity withoutappropriate adjustments to food or insulin intake. Any recommended insulin infusion should not be administered if there is imminent risk of hypoglycemia.

[0120] The Hypo-P module is intended to reduce, and ultimately suspend, any insulin dose based on the associated risk of hypoglycemia. It works by monitoring the user’s blood glucose levels, G, and the recent history of insulin deliveries, U. It continuously computes a maximum allowable upper bound ^^^^^௫^ ^^, ^^^ for insulin infusions based on the observed risk of hypoglycemia. The upper bound, ^^^^^௫^ ^^, ^^^, goes from zero (insulin infusions are suspended due to a severe risk of hypoglycemia) to infinity (insulin doses are not reduced / suspended due to a lack of hypoglycemia risk).

[0121] Modules integration.

[0122] These modules can be integrated into the NAP to bolster safety. If this is the case, then insulin recommendation by the original IDR would be modified by: ^^^^^^ ^^௫^ ൌ min^ ^^^^ ^^ ^^^^ ^^, ^^^, ^^^^ ^^, ^^^^, where ^^^is the ^^^is the additional insulinbolus generated by the BPR, and is the maximum upper bound computed by the Hypo- P.

[0123] These modules can be integrated in the NAP either as an independent module, where the KNN output is modified according to the previous equation– or, also, they can be encoded into the KNN itself. In this last case, the output of the KNN would be directly ^^^^^^^ instead of ^^^. To encode them in the KNN, it should be trained by minimizing the following cost instead of the previous RMSE: ே ^^ ^^^ ^ ^^ ^^^^ ^ ^^^ where ^^ is aேேto zero if ^^^^ ^^^^^௫.

[0124] As can be appreciated from the present disclosure, the NAP method (and related system and computer readable medium) offers a comprehensive and innovative approach to transforming a given IDR, or a plurality of IDRs, into a neural network-based solution, complete with safety modules for hypoglycemia and hyperglycemia protection. By following the steps outlined in this method, it is possible to create a compact, efficient, and safe IDR representation that can be readily deployed in wearable devices, improving the lives of patients who rely on insulin therapy for managing their diabetes.

[0125] An aspect of an embodiment of the present invention provides, among other things, a system, method and computer readable medium for encoding insulin dosing rules into a Neural-net Artificial Pancreas (NAP) intended for the computerized treatment of diabetes.

[0126] An aspect of an embodiment of the present invention provides, among other things, a system, method and computer readable medium for providing compact, efficient, and safe IDR representation that can be readily deployed in wearable devices.

[0127] Practice of an aspect of an embodiment (or embodiments) of the invention will be still more fully understood from the following examples, which are presented herein for illustration only and should not be construed as limiting the invention in any way. Example 1. A Method (and related system or computer readable storage medium) for encoding an Insulin Dosing Rule (IDR), or a plurality of insulin dosing rules, into a Neural- net Artificial Pancreas (NAP) intended for the computerized treatment of diabetes. Example 2. The method (and related system or computer readable storage medium) according to example 1, wherein the insulin dosing rule, or plurality of insulin dosing rules, is described by equations or an algorithm. Example 3. The method (and related system or computer readable storage medium) according to example 1, wherein the insulin dosing rule, or plurality of insulin dosing rules, is encoded in a database or a protocol. Example 4. The method (and related system or computer readable storage medium) according to example 1, wherein the insulin dosing rule, or plurality of insulin dosing rules, is produced via computer simulation of the dosing rule. Example 5. The method (and related system or computer readable storage medium) according to Example 1, further comprising a Nearest Neighbor Graph (NNG), which produces a Saturated Look-up Table (SLT) that accurately represents the dosing rule or plurality of dosing rules of Example 1. Example 6. The method (and related system or computer readable storage medium) according to example 5, wherein the Accurate Representation is defined as both sufficient density and lack of isolated inputs of the insulin dosing rule, or plurality of insulin dosing rules. Example 7. The method (and related system or computer readable storage medium) according to example 6, wherein the Sufficient Density of the inputs of the insulin dosing rule is defined as the NNG having optimal local density measurements within ^^-tolerance.Example 8. The method (and related system or computer readable storage medium) according to example 6, wherein the lack of isolated inputs of the insulin dosing rule is defined as every element in the table having its closest neighbor within ^^-tolerance. Example 9. The method (and related system or computer readable storage medium) according to example 5, comprising Kernel Neural Net (KNN), which uses the SLT of example 5 to encode accurately the insulin dosing rule or plurality of insulin dosing rules, into a neural net framework. Example 10. The method (and related system or computer readable storage medium) according to example 9, wherein the Accurate Encoding is defined as the proximity of the insulin recommendation of the insulin dosing rule or plurality of insulin dosing rules, to the recommendations of the NAP. Example 11. The method (and related system or computer readable storage medium) according to example 10, wherein the proximity is defined as differences between the insulin recommendations of the insulin dosing rule or plurality of insulin dosing rules and the NAP not the maximum metrics reported in Section 4 – Steps 2, 3 of the Detailed Description of this present disclosure. Example 12. The method (and related system or computer readable storage medium) according to example 1, wherein a Safety Rule is incorporated into the NAP Method. Example 13. The method (and related system or computer readable storage medium) according to example 12, wherein Protection against Hypoglycemia (Hypo-P) and hyperglycemia is incorporated into the Safety Rule of the NAP Method. Example 14. The method (and related system or computer readable storage medium) according to example 1, further comprising a Bolus Priming Rule (BPR) that is incorporated into the NAP method. Example 15. The method (and related system or computer readable storage medium) according to example 14, wherein the BPR recommends an amount of pre-meal insulin to be delivered according to the confidence in upcoming meal. Example 16. The method (and related system or computer readable storage medium) according to example 14, wherein the BPR recommends an amount of insulin to be delivered according to the confidence of prevailing hyperglycemia.Example 17. The method (and related system or computer readable storage medium) according to example 16, wherein the confidence in upcoming meal is an estimated probability for a meal in the subsequent half an hour. Example 18. The method (and related system or computer readable medium) according to example 16, wherein the amount of recommended pre-meal insulin is defined by the user according to his / her needs and related IDR. Example 19. A method for: a) encoding insulin dosing rules into a Neural-net Artificial Pancreas (NAP) intended for the computerized treatment of diabetes and / or b) providing compact, efficient, and safe IDR representation that can be readily deployed in wearable devices., as described herein. Example 20. The method according to example 19, including each and every novel feature or combination of features disclosed herein. Example 21. A system configured for a) encoding insulin dosing rules into a Neural- net Artificial Pancreas (NAP) intended for the computerized treatment of diabetes and / or b) providing compact, efficient, and safe IDR representation that can be readily deployed in wearable devices., as described herein. Example 22. The system according to example 21, including each and every novel feature or combination of features disclosed herein. Example 23. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by one or more processors, cause one or more computers to perform functions for a) encoding insulin dosing rules into a Neural-net Artificial Pancreas (NAP) intended for the computerized treatment of diabetes and / or b) providing compact, efficient, and safe IDR representation that can be readily deployed in wearable devices., as described herein. Example 24. The computer-readable storage medium of example 23, including each and every novel feature or combination of features disclosed herein.

[0128] FIGS.8-12 are exemplary system architectures, functional block diagrams, etc. that can be used for embodiments of the system 100, processor 102, or devices 108 disclosed herein.

[0129] FIG.8 is an exemplary high-level functional block diagram for an embodiment of the present invention, or an aspect of an embodiment of the present invention. As shown in FIG.8, a processor 804 or controller communicates with the glucose monitor or data source 812, and optionally an insulin delivery device (e.g., other device 810). The glucose monitor or device communicates with the subject 800 to monitor glucose levels of the subject 800. The processor 804 or controller is configured to perform the required calculations. Optionally, the insulin delivery device communicates with the subject 800 to deliver insulin to the subject 800. The processor 804 or controller is configured to perform the required calculations. The glucose monitor and the insulin delivery device may be implemented as a separate device or as a single device. The processor 804 can be implemented locally in the glucose monitor, the insulin delivery device, or a standalone device (or in any combination of two or more of the glucose monitor, insulin device, or a stand along device). The processor 804 or a portion of the system can be located remotely such that the device is operated as a telemedicine device.

[0130] Referring to FIG.9, in its most basic configuration, computing device 900 typically includes at least one processor 904 and memory 906. Depending on the exact configuration and type of computing device, memory 906 can be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two.

[0131] Additionally, the computing device 900 may also have other features and / or functionality. For example, the computing device 900 could also include additional removable and / or non-removable storage including, but not limited to, magnetic or optical disks or tape, as well as writable electrical storage media. Such additional storage is the figure by removable storage 902a and non-removable storage 902b. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. The memory, the removable storage and the non-removable storage are all examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology CDROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by the device. Any such computer storage media may be part of, or used in conjunction with, the device.

[0132] The computing device 900 may also contain one or more communications connections 908 that allow the device to communicate with other devices (e.g. other computing devices). The communications connections carry information in a communication media. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or othertransport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode, execute, or process information in the signal. By way of example, and not limitation, communication medium includes wired media such as a wired network or direct-wired connection, and wireless media such as radio, RF, infrared and other wireless media. As discussed above, the term computer readable media as used herein includes both storage media and communication media.

[0133] In addition to a stand-alone computing machine, embodiments of the invention can also be implemented on a network system comprising a plurality of computing devices that are in communication with a networking means, such as a network with an infrastructure or an ad hoc network. The network connection can be wired connections or wireless connections. As a way of example, FIG.9 illustrates a network system in which embodiments of the invention can be implemented. In this example, the network system comprises computer 910 (e.g. a network server), network connection means 912 (e.g. wired and / or wireless connections), computer terminal 914, and PDA (e.g. a smart-phone) 916 (or other handheld or portable device, such as a cell phone, laptop computer, tablet computer, GPS receiver, mp3 player, handheld video player, pocket projector, etc. or handheld devices (or non portable devices) with combinations of such features). In an embodiment, it should be appreciated that the module 914 may be glucose monitor device. In an embodiment, it should be appreciated that the module listed as 914 may be a glucose monitor device, artificial pancreas, and / or an insulin device (or other interventional or diagnostic device). Any of the components may be multiple in number. The embodiments of the invention can be implemented in anyone of the devices of the system. For example, execution of the instructions or other desired processing can be performed on the same computing device 900. Alternatively, an embodiment of the invention can be performed on different computing devices of the network system. For example, certain desired or required processing or execution can be performed on one of the computing devices of the network (e.g., server 910 and / or glucose monitor device), whereas other processing and execution of the instruction can be performed at another computing device (e.g., terminal 914) of the network system, or vice versa. In fact, certain processing or execution can be performed at one computing device (e.g. server 910 and / or insulin device, artificial pancreas, or glucose monitor device (or other interventional or diagnostic device)); and the other processing or execution of the instructions can be performed at different computing devices that may or may not be networked. For example, the certain processing can be performed at terminal 914, while the other processing or instructions are passed to a computing device 900 where theinstructions are executed. This scenario may be of particular value especially when the PDA device, for example, accesses to the network through computer terminal 914 (or an access point in an ad hoc network). For another example, software to be protected can be executed, encoded or processed with one or more embodiments of the invention. The processed, encoded or executed software can then be distributed to customers. The distribution can be in a form of storage media (e.g., disk) or electronic copy.

[0134] FIG.10 is a block diagram that illustrates a system 1000 including a computer system 1002 and the associated Internet 1004 connection upon which an embodiment may be implemented. Such configuration is typically used for computers (hosts) connected to the Internet 1004 and executing a server or a client (or a combination) software. A source computer such as laptop, an ultimate destination computer and relay servers, for example, as well as any computer or processor described herein, may use the computer system configuration and the Internet connection shown in FIG.10. The system 1004 may be used as a portable electronic device such as a notebook / laptop computer, a media player (e.g., MP3 based or video player), a cellular phone, a Personal Digital Assistant (PDA), a glucose monitor device, an artificial pancreas, an insulin delivery device (or other interventional or diagnostic device), an image processing device (e.g., a digital camera or video recorder), and / or any other handheld computing devices, or a combination of any of these devices. Note that while FIG. 10 illustrates various components of a computer system, it is not intended to represent any particular architecture or manner of interconnecting the components; as such details are not germane to the present invention. It will also be appreciated that network computers, handheld computers, cell phones and other data processing systems which have fewer components or perhaps more components may also be used. The computer system of FIG.10 may, for example, be an Apple Macintosh computer or Power Book, or an IBM compatible PC. Computer system 1000 includes a bus 1006, an interconnect, or other communication mechanism for communicating information, and a processor, commonly in the form of an integrated circuit, coupled with bus 1006 for processing information and for executing the computer executable instructions. Computer system 1000 also includes a main memory 1008, such as a Random Access Memory (RAM) or other dynamic storage device, coupled to bus 1006 for storing information and instructions to be executed by processor 1010.

[0135] Main memory 1008 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 1010. Computer system 1000 further includes a Read Only Memory (ROM) 1008 (or other non- volatile memory) or other static storage device coupled to bus 1006 for storing staticinformation and instructions for processor 1010. A storage device 1012, such as a magnetic disk or optical disk, a hard disk drive for reading from and writing to a hard disk, a magnetic disk drive for reading from and writing to a magnetic disk, and / or an optical disk drive (such as DVD) for reading from and writing to a removable optical disk, is coupled to bus 1006 for storing information and instructions. The hard disk drive, magnetic disk drive, and optical disk drive may be connected to the system bus by a hard disk drive interface, a magnetic disk drive interface, and an optical disk drive interface, respectively. The drives and their associated computer-readable media provide non-volatile storage of computer readable instructions, data structures, program modules and other data for the general purpose computing devices. Typically computer system 1000 includes an Operating System (OS) stored in a non-volatile storage for managing the computer resources and provides the applications and programs with an access to the computer resources and interfaces. An operating system commonly processes system data and user input, and responds by allocating and managing tasks and internal system resources, such as controlling and allocating memory, prioritizing system requests, controlling input and output devices, facilitating networking and managing files. Non-limiting examples of operating systems are Microsoft Windows, Mac OS X, and Linux.

[0136] The term "processor" is meant to include any integrated circuit or other electronic device (or collection of devices) capable of performing an operation on at least one instruction including, without limitation, Reduced Instruction Set Core (RISC) processors, CISC microprocessors, Microcontroller Units (MCUs), CISC-based Central Processing Units (CPUs), and Digital Signal Processors (DSPs). The hardware of such devices may be integrated onto a single substrate (e.g., silicon "die"), or distributed among two or more substrates. Furthermore, various functional aspects of the processor may be implemented solely as software or firmware associated with the processor.

[0137] Computer system 1000 may be coupled via bus 1006 to a display 1014, such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), a flat screen monitor, a touch screen monitor or similar means for displaying text and graphical data to a user. The display may be connected via a video adapter for supporting the display. The display allows a user to view, enter, and / or edit information that is relevant to the operation of the system. An input device 1016, including alphanumeric and other keys, is coupled to bus 1006 for communicating information and command selections to processor 1010. Another type of user input device is cursor control 1018, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to processor 1010 and for controlling cursor movement on display 1014. This input device typically has two degrees offreedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane.

[0138] The computer system 1002 may be used for implementing the methods and techniques described herein. According to one embodiment, those methods and techniques are performed by computer system 1002 in response to processor 1010 executing one or more sequences of one or more instructions contained in main memory 1020. Such instructions may be read into main memory 1022 from another computer-readable medium, such as storage device 1012. Execution of the sequences of instructions contained in main memory 1022 causes processor 1010 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the arrangement. Thus, embodiments of the invention are not limited to any specific combination of hardware circuitry and software.

[0139] The term "computer-readable medium" (or "machine-readable medium") as used herein is an extensible term that refers to any medium or any memory, that participates in providing instructions to a processor, (such as processor 1010) for execution, or any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer). Such a medium may store computer-executable instructions to be executed by a processing element and / or control logic, and data which is manipulated by a processing element and / or control logic, and may take many forms, including but not limited to, non- volatile medium, volatile medium, and transmission medium. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 1006. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infrared data communications, or other form of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch-cards, paper-tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH- EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

[0140] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processor 1010 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 1000 can receive the dataon the telephone line and use an infra-red transmitter to convert the data to an infra-red signal. An infra-red detector can receive the data carried in the infra-red signal and appropriate circuitry can place the data on bus 1006. Bus 1006 carries the data to main memory 1022, from which processor 1010 retrieves and executes the instructions. The instructions received by main memory 1022 may optionally be stored on storage device 1012 either before or after execution by processor 1010.

[0141] Computer system 1000 also includes a communication interface 1024 coupled to bus 1006. Communication interface 1024 provides a two-way data communication coupling to a network link 1026 that is connected to a local network 1028. For example, communication interface 1024 may be an Integrated Services Digital Network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another non-limiting example, communication interface 1024 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. For example, Ethernet based connection based on IEEE802.3 standard may be used such as 10 / 100BaseT, 1000BaseT (gigabit Ethernet), 10 gigabit Ethernet (10 GE or 10 GbE or 10 GigE per IEEE Std 802.3ae-2002 as standard), 40 Gigabit Ethernet (40 GbE), or 100 Gigabit Ethernet (100 GbE as per Ethernet standard IEEE P802.3ba), as described in Cisco Systems, Inc. Publication number 1-587005-001-3 (6 / 99), "Internetworking Technologies Handbook", Chapter 7: "Ethernet Technologies", pages 7-1 to 7-38, which is incorporated in its entirety for all purposes as if fully set forth herein. In such a case, the communication interface 1818 typically include a LAN transceiver or a modem, such as Standard Microsystems Corporation (SMSC) LAN91C11110 / 100 Ethernet transceiver described in the Standard Microsystems Corporation (SMSC) data-sheet "LAN91C11110 / 100 Non-PCI Ethernet Single Chip MAC+PHY" Data- Sheet, Rev.15 (02-20-04), which is incorporated in its entirety for all purposes as if fully set forth herein.

[0142] Wireless links may also be implemented. In any such implementation, communication interface 1024 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0143] Network link 1026 typically provides data communication through one or more networks to other data devices. For example, network link 1026 may provide a connection through local network 1028 to a host computer or to data equipment operated by an Internet Service Provider (ISP) 1025. ISP 1025 in turn provides data communication services through the world wide packet data communication network Internet 1004. Local network 1028 and Internet 1004 both use electrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals on the network link 1026 and through the communication interface 1024, which carry the digital data to and from computer system 1000, are exemplary forms of carrier waves transporting the information.

[0144] A received code may be executed by processor 1010 as it is received, and / or stored in storage device 1012, or other non-volatile storage for later execution. In this manner, computer system 1000 may obtain application code in the form of a carrier wave.

[0145] FIG.11 illustrates a system in which one or more embodiments of the invention can be implemented using a network, or portions of a network or computers. Although the present invention glucose monitor, artificial pancreas or insulin device (or other interventional or diagnostic device) may be practiced without a network. FIG.11 diagrammatically illustrates an exemplary system in which examples of the invention can be implemented. In an embodiment the glucose monitor, artificial pancreas or insulin device (or other interventional or diagnostic device) may be implemented by the subject (or patient) locally at home or other desired location. However, in an alternative embodiment it may be implemented in a clinic setting or assistance setting. For instance, a clinic setup 1100 provides a place for doctors (e.g. 1102) or clinician / assistant to diagnose patients (e.g.1104) with diseases related with glucose and related diseases and conditions. A glucose monitoring device 1106 can be used to monitor and / or test the glucose levels of the patient—as a standalone device. It should be appreciated that while only glucose monitor device 1106 is shown in the figure, the system of the invention and any component thereof may be used in the manner depicted by FIG.11. The system or component may be affixed to the patient or in communication with the patient as desired or required. For example the system or combination of components thereof - including a glucose monitor device 1106 (or other related devices or systems such as a controller, and / or an artificial pancreas, an insulin pump (or other interventional or diagnostic device), or any other desired or required devices or components) - may be in contact, communication or affixed to the patient through tape or tubing (or other medical instruments or components) or may be in communication through wired or wireless connections. Such monitor and / or test can be short term (e.g. clinical visit) or long term (e.g. clinical stay or family). The glucose monitoring device outputs can be used by the doctor (clinician or assistant) for appropriate actions, such as insulin injection or food feeding for the patient, or other appropriate actions or modeling. Alternatively, the glucose monitoring device output can be delivered to computer terminal 1112 for instant or future analyses. The delivery can be through cable or wireless or any other suitable medium. The glucose monitoring device output from the patient can also be delivered to a portable device, such as PDA 1110. The glucose monitoring device outputs withimproved accuracy can be delivered to a glucose monitoring center 1112 for processing and / or analyzing. Such delivery can be accomplished in many ways, such as network connection 1114, which can be wired or wireless.

[0146] In addition to the glucose monitoring device outputs, errors, parameters for accuracy improvements, and any accuracy related information can be delivered, such as to computer and / or glucose monitoring center 1112 for performing error analyses. This can provide a centralized accuracy monitoring, modeling and / or accuracy enhancement for glucose centers (or other interventional or diagnostic centers), due to the importance of the glucose sensors (or other interventional or diagnostic sensors or devices).

[0147] Examples of the invention can also be implemented in a standalone computing device associated with the target glucose monitoring device, artificial pancreas, and / or insulin device (or other interventional or diagnostic device.

[0148] FIG.12 is a block diagram illustrating an example of a machine upon which one or more aspects of embodiments of the present invention can be implemented. Referring to FIG. 12, an aspect of an embodiment of the present invention includes, but not limited thereto, a system, method, and computer readable medium, which illustrates a block diagram of an example machine 1200 upon which one or more embodiments (e.g., discussed methodologies) can be implemented (e.g., run).

[0149] FIG.12 illustrates a block diagram of an example machine 1200 upon which one or more embodiments (e.g., discussed methodologies) can be implemented (e.g., run).

[0150] Examples of machine 1200 can include logic, one or more components, circuits (e.g., modules), or mechanisms. Circuits are tangible entities configured to perform certain operations. In an example, circuits can be arranged (e.g., internally or with respect to external entities such as other circuits) in a specified manner. In an example, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware processors (processors) can be configured by software (e.g., instructions, an application portion, or an application) as a circuit that operates to perform certain operations as described herein. In an example, the software can reside (1) on a non-transitory machine readable medium or (2) in a transmission signal. In an example, the software, when executed by the underlying hardware of the circuit, causes the circuit to perform the certain operations.

[0151] In an example, a circuit can be implemented mechanically or electronically. For example, a circuit can comprise dedicated circuitry or logic that is specifically configured to perform one or more techniques such as discussed above, such as including a special-purpose processor, a field programmable gate array (FPGA) or an application-specific integrated circuit(ASIC). In an example, a circuit can comprise programmable logic (e.g., circuitry, as encompassed within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform the certain operations. It will be appreciated that the decision to implement a circuit mechanically (e.g., in dedicated and permanently configured circuitry), or in temporarily configured circuitry (e.g., configured by software) can be driven by cost and time considerations.

[0152] Accordingly, the term “circuit” is understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily (e.g., transitorily) configured (e.g., programmed) to operate in a specified manner or to perform specified operations. In an example, given a plurality of temporarily configured circuits, each of the circuits need not be configured or instantiated at any one instance in time. For example, where the circuits comprise a general-purpose processor configured via software, the general-purpose processor can be configured as respective different circuits at different times. Software can accordingly configure a processor, for example, to constitute a particular circuit at one instance of time and to constitute a different circuit at a different instance of time.

[0153] In an example, circuits can provide information to, and receive information from, other circuits. In this example, the circuits can be regarded as being communicatively coupled to one or more other circuits. Where multiple of such circuits exist contemporaneously, communications can be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the circuits. In embodiments in which multiple circuits are configured or instantiated at different times, communications between such circuits can be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple circuits have access. For example, one circuit can perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further circuit can then, at a later time, access the memory device to retrieve and process the stored output. In an example, circuits can be configured to initiate or receive communications with input or output devices and can operate on a resource (e.g., a collection of information).

[0154] The various operations of method examples described herein can be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute processor-implemented circuits that operate to perform one or more operations or functions. In an example, the circuits referred to herein can comprise processor-implemented circuits.

[0155] Similarly, the methods described herein can be at least partially processor- implemented. For example, at least some of the operations of a method can be performed by one or processors or processor-implemented circuits. The performance of certain of the operations can be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In an example, the processor or processors can be located in a single location (e.g., within a home environment, an office environment or as a server farm), while in other examples the processors can be distributed across a number of locations.

[0156] The one or more processors can also operate to support performance of the relevant operations in a "cloud computing" environment or as a "software as a service” (SaaS). For example, at least some of the operations can be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., Application Program Interfaces (APIs).)

[0157] Example embodiments (e.g., apparatus, systems, or methods) can be implemented in digital electronic circuitry, in computer hardware, in firmware, in software, or in any combination thereof. Example embodiments can be implemented using a computer program product (e.g., a computer program, tangibly embodied in an information carrier or in a machine readable medium, for execution by, or to control the operation of, data processing apparatus such as a programmable processor, a computer, or multiple computers).

[0158] A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand- alone program or as a software module, subroutine, or other unit suitable for use in a computing environment. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a communication network.

[0159] In an example, operations can be performed by one or more programmable processors executing a computer program to perform functions by operating on input data and generating output. Examples of method operations can also be performed by, and example apparatus can be implemented as, special purpose logic circuitry (e.g., a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)).

[0160] The computing system can include clients and servers. A client and server are generally remote from each other and generally interact through a communication network. The relationship of client and server arises by virtue of computer programs running on therespective computers and having a client-server relationship to each other. In embodiments deploying a programmable computing system, it will be appreciated that both hardware and software architectures require consideration. Specifically, it will be appreciated that the choice of whether to implement certain functionality in permanently configured hardware (e.g., an ASIC), in temporarily configured hardware (e.g., a combination of software and a programmable processor), or a combination of permanently and temporarily configured hardware can be a design choice. Below are set out hardware (e.g., machine 1200) and software architectures that can be deployed in example embodiments.

[0161] In an example, the machine 1200 can operate as a standalone device or the machine 1200 can be connected (e.g., networked) to other machines.

[0162] In a networked deployment, the machine 1200 can operate in the capacity of either a server or a client machine in server-client network environments. In an example, machine 1200 can act as a peer machine in peer-to-peer (or other distributed) network environments. The machine 1200 can be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) specifying actions to be taken (e.g., performed) by the machine 1200. Further, while only a single machine 1200 is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[0163] Example machine (e.g., computer system) 1200 can include a processor 1250 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 1252a and a static memory 1252b, some or all of which can communicate with each other via a bus 1220. The machine 1200 can further include a display unit 1202, an alphanumeric input device 1204 (e.g., a keyboard), and a user interface (UI) navigation device 1206 (e.g., a mouse). In an example, the display unit 1202, input device 1204 and UI navigation device 1206 can be a touch screen display. The machine 1200 can additionally include a storage device (e.g., drive unit) 1208, a signal generation device 1210 (e.g., a speaker), a network interface device 1212, and one or more sensors 1214, such as a global positioning system (GPS) sensor, compass, accelerometer, or other sensor.

[0164] The storage device 1208 can include a machine readable medium 1216 on which is stored one or more sets of data structures or instructions 1254 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 1254 can also reside, completely or at least partially, within the main memory1252a, within static memory 1252b, or within the processor 804 during execution thereof by the machine 1200. In an example, one or any combination of the processor 804, the main memory 1252a, the static memory 1252b, or the storage device 1208 can constitute machine readable media.

[0165] While the machine readable medium 1216 is illustrated as a single medium, the term "machine readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database, and / or associated caches and servers) that configured to store the one or more instructions 1254. The term “machine readable medium” can also be taken to include any tangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions. The term “machine readable medium” can accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media. Specific examples of machine readable media can include non-volatile memory, including, by way of example, semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0166] The instructions 1254 can further be transmitted or received over a communications network 1218 using a transmission medium via the network interface device utilizing any one of a number of transfer protocols (e.g., frame relay, IP, TCP, UDP, HTTP, etc.). Example communication networks can include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., IEEE 802.11 standards family known as Wi-Fi®, IEEE 802.16 standards family known as WiMax®), peer-to-peer (P2P) networks, among others. The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

[0167] Although example embodiments of the present disclosure are explained in some instances in detail herein, it is to be understood that other embodiments are contemplated. Accordingly, it is not intended that the present disclosure be limited in its scope to the details of construction and arrangement of components set forth in the following description orillustrated in the drawings. The present disclosure is capable of other embodiments and of being practiced or carried out in various ways.

[0168] It should be appreciated that any element, part, section, subsection, or component described with reference to any specific embodiment above may be incorporated with, integrated into, or otherwise adapted for use with any other embodiment described herein unless specifically noted otherwise or if it should render the embodiment device non- functional. Likewise, any step described with reference to a particular method or process may be integrated, incorporated, or otherwise combined with other methods or processes described herein unless specifically stated otherwise or if it should render the embodiment method nonfunctional. Furthermore, multiple embodiment devices or embodiment methods may be combined, incorporated, or otherwise integrated into one another to construct or develop further embodiments of the invention described herein.

[0169] It should be appreciated that any of the components or modules referred to with regards to any of the present invention embodiments discussed herein, may be integrally or separately formed with one another. Further, redundant functions or structures of the components or modules may be implemented. Moreover, the various components may be communicated locally and / or remotely with any user / clinician / patient or machine / system / computer / processor. Moreover, the various components may be in communication via wireless and / or hardwire or other desirable and available communication means, systems and hardware. Moreover, various components and modules may be substituted with other modules or components that provide similar functions.

[0170] It should be appreciated that the device and related components discussed herein may take on all shapes along the entire continual geometric spectrum of manipulation of x, y and z planes to provide and meet the anatomical, environmental, and structural demands and operational requirements. Moreover, locations and alignments of the various components may vary as desired or required.

[0171] It should be appreciated that various sizes, dimensions, contours, rigidity, shapes, flexibility and materials of any of the components or portions of components in the various embodiments discussed throughout may be varied and utilized as desired or required.

[0172] It should be appreciated that while some dimensions are provided on the aforementioned figures, the device may constitute various sizes, dimensions, contours, rigidity, shapes, flexibility and materials as it pertains to the components or portions of components of the device, and therefore may be varied and utilized as desired or required.

[0173] It must also be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from “about” or “approximately” one particular value and / or to “about” or “approximately” another particular value. When such a range is expressed, other exemplary embodiments include from the one particular value and / or to the other particular value.

[0174] By “comprising” or “containing” or “including” is meant that at least the named compound, element, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, or method steps, even if the other such compounds, material, particles, or method steps have the same function as what is named.

[0175] In describing example embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents that operate in a similar manner to accomplish a similar purpose. It is also to be understood that the mention of one or more steps of a method does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Steps of a method may be performed in a different order than those described herein without departing from the scope of the present disclosure. Similarly, it is also to be understood that the mention of one or more components in a device or system does not preclude the presence of additional components or intervening components between those components expressly identified.

[0176] Some references, which may include various patents, patent applications, and publications, are cited in a reference list and discussed in the disclosure provided herein. The citation and / or discussion of such references is provided merely to clarify the description of the present disclosure and is not an admission that any such reference is “prior art” to any aspects of the present disclosure described herein. In terms of notation, “[n]” corresponds to the nthreference in the list. All references cited and discussed in this specification are incorporated herein by reference in their entireties and to the same extent as if each reference was individually incorporated by reference.

[0177] It should be appreciated that as discussed herein, a subject may be a human or any animal. It should be appreciated that an animal may be a variety of any applicable type, including, but not limited thereto, mammal, veterinarian animal, livestock animal or pet type animal, etc. As an example, the animal may be a laboratory animal specifically selected tohave certain characteristics similar to human (e.g. rat, dog, pig, monkey), etc. It should be appreciated that the subject may be any applicable human patient, for example.

[0178] The term “about,” as used herein, means approximately, in the region of, roughly, or around. When the term “about” is used in conjunction with a numerical range, it modifies that range by extending the boundaries above and below the numerical values set forth. In general, the term “about” is used herein to modify a numerical value above and below the stated value by a variance of 10%. In one aspect, the term “about” means plus or minus 10% of the numerical value of the number with which it is being used. Therefore, about 50% means in the range of 45%-55%. Numerical ranges recited herein by endpoints include all numbers and fractions subsumed within that range (e.g.1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, 4.24, and 5). Similarly, numerical ranges recited herein by endpoints include subranges subsumed within that range (e.g.1 to 5 includes 1-1.5, 1.5-2, 2-2.75, 2.75-3, 3-3.90, 3.90-4, 4-4.24, 4.24- 5, 2-5, 3-5, 1-4, and 2-4). It is also to be understood that all numbers and fractions thereof are presumed to be modified by the term “about.”

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[0181] It will be understood that modifications to the embodiments disclosed herein can be made to meet a particular set of design criteria. For instance, any of the components, features, or steps of the system o method can be any suitable number or type of each to meet a particular objective. Therefore, while certain exemplary embodiments of the systems and methods disclosed herein have been discussed and illustrated, it is to be distinctly understood that the invention is not limited thereto but can be otherwise variously embodied and practiced within the scope of the following claims.

[0182] It will be appreciated that some components, features, and / or configurations can be described in connection with only one particular embodiment, but these same components, features, and / or configurations can be applied or used with many other embodiments and should be considered applicable to the other embodiments, unless stated otherwise or unless such a component, feature, and / or configuration is technically impossible to use with the other embodiments. Thus, the components, features, and / or configurations of the various embodiments can be combined in any manner and such combinations are expressly contemplated and disclosed by this statement

[0183] It will be appreciated by those skilled in the art that the present invention can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The presently disclosed embodiments are therefore considered in all respects to be illustrative and not restrictive. The scope of the invention is indicated by the appended claims rather than the foregoing description and all changes that come within the meaning, range, andequivalence thereof are intended to be embraced therein. Additionally, the disclosure of a range of values is a disclosure of every numerical value within that range, including the end points.

Claims

WHAT IS CLAIMED IS:

1. A system for developing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing, the system comprising: a processor; computer memory having instructions stored thereon that when executed will cause the processor to: receive an insulin dosing rule (IDR) including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us); generate a saturated data set with the plural Xs and corresponding Us so that the saturated data set has optimal data density; generate a saturated look-up table (STL) with the saturated data set; and encode the STL into a neural network.

2. The system of claim 1, wherein the instructions will cause the processor to: receive the IDR from the computer memory or from a data store.

3. The system of claim 1, wherein the instructions will cause the processor to: receive plural IDRs.

4. The system of claim 1, wherein the instructions will cause the processor to: generate plural STLs.

5. The system of claim 1, wherein the instructions will cause the processor to: receive a measurement indicative of a metabolic state (Xm) from a glucose monitoring device or a glucose measurement device and identify a corresponding U using at least one or more of a simulation technique, a predictive technique, an interpolation technique, a regression technique, or a nearest neighbor technique; include the Xm and corresponding U with the saturated data set to populate a lower density region.

6. The system of claim 5, wherein the instructions will cause the processor to: receive a Xm from a glucose monitoring device or a glucose measurement device and identify a corresponding U using the nearest neighbor technique, the nearest neighbor technique being a nearest neighbor graph technique.

7. The system of claim 1, wherein the instructions will cause the processor to: encode the STL into a kernel neural network.

8. The system of claim 1, wherein the instructions will cause the processor to: train the neural network with a cost function.

9. The system of claim 1, wherein the instructions will cause the processor to: encode at least one or more of a hypoglycemia protection rule or a bolus priming rule into the STL.

10. A system for efficiently processing and applying insulin dosing rules to metabolic data to generate optimized insulin dosing, the system comprising: a processor; computer memory having instructions stored thereon that when executed will cause the processor to: receive a measurement indicative of a metabolic state (Xm); implement a model, the model including: a saturated look-up table (SLT) encoded into a neural network, the SLT including a saturated data set of optimal data density, the saturated data set including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us) from an insulin dosing rule (IDR); wherein implementation of the model applies the Xm to the SLT to identify a corresponding U; and monitor, analyze, or influence a concentration of glucose levels in a fluid using the identified U.

11. The system of claim 10, wherein: the instructions will cause the processor to receive the model as a streaming software application;the model is stored on the computer memory as a software application and the instructions will cause the processor to retrieve the model therefrom; or the model is stored in a data store as a software application and the instructions will cause the processor to retrieve the model therefrom.

12. The system of claim 10, wherein the processor is configured to be a component of, used in combination with, or in communication with at least one or more of: a predictive modeling system, a decision support system, or an automated control system.

13. The system of claim 10, wherein the processor is configured to be a component of, used in combination with, or in communication with at least one or more of: a non-portable computer device, a portable computer device, a hand-held computer device, or a wearable computer device.

14. The system of claim 10, wherein the processor is configured to be a component of, used in combination with, or in communication with: an insulin monitoring device or an insulin delivery device.

15. The system of claim 10, wherein the processor is configured to be a component of, used in combination with, or in communication with: an insulin delivery device; and the instructions will cause the processor to generate a command signal configured to cause the insulin delivery device to administer or adjust an insulin dosage amount or an insulin dosage rate based on the identified U.

16. The system of claim 10, wherein the model includes: plural STLs; and one or more SLTs include a saturated data set of plural Xs and corresponding Us from one or more IDRs.

17. The system of claim 10, wherein: applying the Xm to the SLT to identify a corresponding U includes using at least one or more of a simulation technique, a predictive technique, an interpolation technique, a regression technique, or a nearest neighbor technique.

18. The system of claim 10, wherein: the STL is encoded with at least one or more of a hypoglycemia protection rule or a bolus priming rule; and the instructions will cause the processor to monitor, analyze, or influence a concentration of glucose levels using the identified U and the at least one or more of the hypoglycemia protection rule or the bolus priming rule.

19. A method for developing a model to efficiently process and apply insulin dosing rules to metabolic data to generate optimized insulin dosing, the method comprising: receiving an insulin dosing rule (IDR) including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us); generating a saturated data set with the plural Xs and corresponding Us so that the saturated data set has optimal data density; generating a saturated look-up table (STL) with the saturated data set; and encoding the STL into a neural network.

20. A method for efficiently processing and applying insulin dosing rules to metabolic data to generate optimized insulin dosing, the method comprising: receiving a measurement indicative of a metabolic state (Xm); implementing a model, the model including: a saturated look-up table (SLT) encoded into a neural network, the SLT including a saturated data set of optimal data density, the saturated data set including plural metabolic states (Xs) and corresponding insulin dosage recommendations (Us) from an insulin dosing rule (IDR); wherein implementation of the model applies the Xm to the SLT to identify a corresponding U; and monitoring, analyzing, or influencing a concentration of glucose levels in a fluid using the identified U.

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