32 results about "Glucose Measurement" patented technology

Detection of anomalous computing environment behavior using glucose is described. An anomaly detection system receives glucose measurements and event records during a first time period. Missing events that are missing from the event records during the first time period are identified by processing the glucose measurements using an event engine simulator. An anomaly detection model is generated based on the missing events during the first time period. Subsequently, the anomaly detection system receives additional glucose measurements and additional event records during a second time period. Missing events that are missing from the additional event records during the second time period are identified by processing the additional glucose measurements using the event engine simulator. Anomalous behavior is detected if the identified missing events that are missing from the event records during the second time period are outside a predicted range of missing events of the anomaly detection model.

Glucose level measurements of a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements can be produced substantially continuously, such that the device may be configured to produce the glucose level measurements at regular or irregular intervals of time, responsive to establishing a communicative coupling with a different device, and so forth. These glucose level measurements are analyzed to detect deviations from past glucose measurements, such as glucose measurements received earlier in the day or glucose measurements received at corresponding times of one or more preceding days. Indications of detected deviations are provided to the user or communicated elsewhere, such as to a healthcare professional.

The present invention relates to a computer-implemented method for aiding in the guidance of diabetes drug therapy of an individual suffering from diabetes mellitus. The method is based on glucose data from said individual that are received at a processing unit, wherein said glucose data comprise at least three fasting glucose measurement values from said individual, and / or at least three matched pre-prandial glucose measurement values and postprandial glucose measurement values at the same meal from said individual. Further contemplated by the present invention is a device for aiding in the guidance of drug diabetes therapy of an individual suffering from diabetes, said device comprising a processing unit, and a computer program including computer-executable instructions, wherein said instructions, when executed by the processing unit, causes the processing unit to perform the computer-implemented method of the present invention.

This invention provides an improved method for electrochemical glucose measurement and an improved composition for glucose measurement. This invention also provides a composition for glucose measurement comprising a flavin compound. This invention also provides an electrode comprising a flavin compound and a sensor and a glucose fuel cell comprising such electrode. In addition, this invention provides a method for electrochemical glucose measurement and a method for electric power generation using a flavin compound. The flavin compound of the present invention is non-toxic or less toxic and it can be thus used for a means of self-contained continuous glucose measurement.

A method of determining an insulin bolus to be administered by an insulin delivering device is disclosed. According to the inventive method, glucose measurement data corresponding to glucose level measurement signals detected by a continuous glucose sensor are received. An insulin bolus to be administered by the insulin delivering device is determined based on a formula having expressions indicative of, for example, glucose level derived from the glucose measurement data, carbohydrate-to-insulin ratio (CIR) and an insulin sensitivity factor (ISF). The method can be carried out using a processor of a device such as insulin pen, an insulin pump, a remote control device configured to remotely control an insulin pump or an insulin pen, a continuous glucose meter, a controller of a continuous glucose meter, a blood glucose meter, and a portable electronic device such as a smart phone, smart watch or tablet.

The present invention provides a biosensor for detecting glucose in saliva by using a saliva glucose measurement device, the biosensor comprising: an electrically insulated substrate; a plurality of electrodes disposed on the substrate and including a working electrode and a reference electrode for detecting glucose in the saliva; and a reagent layer disposed on the plurality of electrodes, wherein the reagent layer is disposed in a saliva inflow path which is located in the biosensor and configured to allow the saliva to flow thereinto. The reagent layer includes glucose dehydrogenase (GDH) and an electron transfer mediator, the saliva glucose measurement device is configured to apply a voltage to the working electrode and the reference electrode, the working electrode and the reference electrode are configured to transmit, to the saliva glucose measurement device, a current signal generated due to an oxidation / reduction reaction between the glucose dehydrogenase and the glucose in the saliva according to the applied voltage, and the value of the applied voltage is included in a numerical range of 150 mV to 300 mV.

In an embodiment, a system for mitigating diabetic ketoacidosis (DKA) risk for a patient in real-time includes a continuous glucose monitoring (CGM) system configured to generate one or more glucose measurements associated with a cunent glucose level of a patient over a period, one or more memories including executable instructions, and one or more processors in data communication with the one or more memories. The one or more processors are configured to execute the executable instructions to receive glucose measurements for the patient from the CGM system, receive insulin data for the patient, evaluate criteria associated with DKA using first information related to the glucose measurements and second information related to the insulin data, detect a risk of DKA for the patient responsive to a combination of the first information and the second information satisfying the criteria associated with DKA, and alert the patient based on the detected risk.

A method, system, and computer readable medium for a dual mode closed loop control (CLC) system is provided that integrates each of (i) an adaptive, individualized model predictive control (MPC) control law that adjusts the control strength of insulin infusion as a function of recent past control actions, glucose measurements, and derivatives thereof, (ii) an automatic bolus priming system (BPS) that commands an extra insulin injection upon detection of a viable metabolic condition (e.g., an unannounced meal), and (iii) a hyperglycemia mitigation system (HMS) to avoid pervasive hyperglycemia.

Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, an observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data for a population of users to predict a diabetes classification for an individual user. The historical glucose measurements for the population of users can be provided by glucose monitoring devices worn by users of the population of users, while the historical outcome data includes one or more diagnostic measurements obtained from a source independent of the glucose monitoring devices. After training is complete, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device over an observation period spanning multiple days. The predicted diabetes classification can then be output, such as by generating one or more notifications or user interfaces based on the classification.

A method, system, and computer-readable medium are provided for a dual mode closed loop control (CLC) system that integrates each of (i) an adaptive, personalized model predictive control (MPC) control law that adjusts the control intensity of insulin infusion according to recent past control actions, glucose measurements, and derivatives thereof, and (ii) an adaptive, personalized model predictive control (MPC) control law that adjusts the control intensity of insulin infusion according to the recent past control actions, glucose measurements, and derivatives thereof. In one embodiment, the system includes (i) an insulin injection system (e.g., an automated meal dose initiation system (BPS) that commands additional insulin injection upon detection of viable metabolic conditions (e.g., unnotified feeding), and (iii) a hyperglycemia relief system (HMS) to avoid prevalent hyperglycemia.

Data describing glucose measurements is received from a continuous glucose monitoring (CGM) system worn by a user and predicted glucose values during a future time period are generated for the user based on the data. A determination is made that at least one of the predicted glucose values satisfies a threshold value for an alert, which is associated with a prediction horizon that defines an amount of time prior to satisfaction of the threshold value for communicating the alert to the user. Output of the alert is caused responsive to determining that the at least one predicted glucose value satisfies the threshold value for the alert within the prediction horizon, relative to a current time. The prediction horizon is modified based on a user response to the alert. Output of a subsequent instance of the alert is caused based on the modified prediction horizon.

In an aspect, the present disclosure provides an insulin delivery device configured to be positioned on a body of a subject via a single puncture site in said body of said subject, wherein said insulin delivery device is configured to deliver an insulin or insulin analog formulation to said subject via said single puncture site, wherein said insulin delivery device comprises a sensor configured to measure a glucose concentration in said body of said subject via said single puncture site, and wherein said delivering and said measuring are performed at substantially the same time for a time period of at least 1 hour subsequent to said positioning, while maintaining a sensitivity of said sensor of at least 50% of an initial sensitivity of said sensor.

Methods and systems for encouraging interactions with a glucose monitoring system include incrementing a score and / or providing a reward based on a variety of different interactions with the glucose monitoring system. The interactions which improve the score may include initiating or changing displays, downloading data, setting operational parameters and other interactions that are independent of a user's glucose measurements. Encouraging these interactions can enhance success in maintaining healthy glucose concentrations.

A method, system, and computer-readable medium are provided for a dual mode Closed-Loop Control (CLC) system integrating each of (i) an adaptive, personalized Model Predictive Control (MPC) control law that modulates the control strength of insulin infusion depending on recent past control actions, glucose measurements, and their derivative(s), (ii) an automatic Bolus Priming System (BPS) that commands additional insulin injections upon the detection of enabling metabolic conditions (e.g., an unannounced meal), and (iii) a hyperglycemia mitigation system (HMS) to avoid prevailing hyperglycemia.

Multi-state engagement with continuous glucose monitoring (CGM) systems is described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains packages of glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform generates state information for the user by processing these CGM packages and the additional data, at least in part, by using one or more models. Based on this state information, the data analytics platform controls communication with the user, which may include generating intervention strategies to prevent users from transitioning to a negative state such as discontinuing use of the CGM system.

Presented is a medicament delivery system including a controller, a wearable medication delivery device, an analyte sensor, and other devices. In an example, a receiver is operable to receive glucose measurement values from the analyte sensor. A number of devices of the medicament delivery system may include a processor operable to execute programming code and to identify anomalous readings among the received glucose measurement values. The identified anomalous reading may, for example, be based on the received glucose measurement values violating a measurement change threshold indicative of abnormal sensor behavior. The processor may be operable to, in response to identifying the anomalous reading, revert a medicament delivery setting to a default safety basal delivery setting and / or generate a notification, an inquiry, and / or an alert for presentation to a user.

Various systems and methods are disclosed. One or more of the methods disclosed uses machine learning algorithms to predict biophysical responses from biophysical data, such as heart rate monitor data, food logs, or glucose measurements. Biophysical responses may include behavioral responses. Additional systems and methods extract nutritional information from food items by parsing strings containing names of food items.

A device for continuously monitoring glucose levels in a patient includes a lead assembly in electrical communication with an electronics assembly. The electronics assembly is configured to be positioned in the subcutaneous tissue and the lead assembly is configured to be positioned in a vessel of the patient, such as in a vein or in an epidural space. The lead assembly has a lumen, and includes at least one electrode in electrical communication with the electronics assembly.

Glucose level measurements of a user are obtained over time, such as from a wearable glucose monitoring device being worn by the user. These glucose level measurements can be produced substantially continuously, such that the device may be configured to produce the glucose level measurements at regular or irregular intervals of time, responsive to establishing a communicative coupling with a different device, and so forth. These glucose level measurements are analyzed to detect deviations from past glucose measurements, such as glucose measurements received earlier in the day or glucose measurements received at corresponding times of one or more preceding days. Indications of detected deviations are provided to the user or communicated elsewhere, such as to a healthcare professional.

Disclosed are an apparatus, system, processes, and techniques for adapting drug delivery and monitoring a user's safety when the user is experiencing a sick day. A processor may be operable to provide a graphical user interface that presents a sickness mode prompt for a user to indicate that the user or another person with diabetes is sick. The graphical user interface may present symptoms from which a user may select. Based on the selection of symptoms, glucose measurement values, and / or ketone measurement values, a response to the user's indication of sickness may be provided.

Methods, devices and system for determining fasting glucose level information and post-prandial glucose level information for diagnosing pre-diabetic and diabetic conditions based on monitored glucose measurements are provided.

Methods and solutions are disclosed for the quantification of mixed-linkage beta-glucan in plant biomass samples. Improved alkaline pretreatment, enzymatic hydrolysis, and glucose measurement parameters are provided that increase the accuracy, precision, and sensitivity of mixed-linkage beta-glucan assays.

In some embodiments, a method of automatically adjusting insulin therapy for a patient includes receiving, at a device associated with the patient, one or more glucose measurements generated by a continuous glucose monitoring (CGM) system of the patient for a period. The method also includes determining, at the device, basal insulin data and bolus insulin data of the patient for the period. The method also includes determining, at the device, a relationship between the basal insulin data and the bolus insulin data. The method also includes generating, at the device, an insulin therapy instruction for the patient based on the one or more glucose measurements and the determined relationship between the basal insulin data and the bolus insulin data, where the insulin therapy instruction includes a total bolus amount.

Certain aspects of the present disclosure provide systems and techniques for rapid detection of repetitive metabolic events in a host based on measured analyte data provided by an analyte monitor worn by the host. An example system is configured to obtain measured glucose data of the host. A subset of the measured glucose data is determined, based on performing a filtering operation on the measured glucose data. A respective range of a likelihood of an occurrence of a metabolic is determined for each value within the subset of the measured glucose data. For at least one value within the subset of the measured glucose data, a state of the metabolic event is determined based in part on at least one of an upper bound or a lower bound of the respective range corresponding to the at least one value within the subset of the measured glucose data.

The present invention relates to an enzyme electrode for glucose measurement. The enzyme electrode comprises an electrode substrate layer and a sensing layer covering the electrode substrate layer, wherein the sensing layer comprises: an NAD(P)(H)-dependent glucose oxidoreductase and an NAD(P)(H) oxidoreductase. The enzyme electrode further comprises a first polymer layer covering the sensing layer, the first polymer layer being used for protecting the sensing layer and / or restricting the permeation of glucose and / or an interferant. The enzyme electrode further comprises NAD(P)(H) which is located on the sensing layer; and / or an NAD(P)(H) derivative which is a polymer covalently bound to NAD(P)(H) and located on the sensing layer and / or the first polymer layer. The enzyme electrode of the present invention can achieve stable, accurate and / or anti-interference continuous measurement of the blood glucose level.

The present disclosure teaches methods for optical estimation of blood glucose. In some embodiments, the disclosed methods involve estimating glucose levels based on absorbance detection of at least one electrolyte. Also provided are apparatuses for optical estimation of blood glucose, including instructions for use. In some embodiments, the apparatuses include light emitting units configured to emit light at specific wavelengths.

Introduced here are diabetes management platforms able to examine glucose measurements associated with a subject to discover patterns indicative of similar behaviors / circumstances. More specifically, a diabetes management platform can acquire multiple data series generated by a glucose monitoring device that monitors the blood glucose level of an individual over multiple time intervals. The diabetes management platform can then apply a similarity algorithm to produce a distance measure (also referred to as a “similarity measure”) for each data series. For example, the diabetes management platform may, for each data series, perform dynamic time warping to produce a distance measure with respect to each of the other data series. The diabetes management platform can identify patterns indicative of similar behaviors / circumstances based on these distance measures.

Multi-state engagement with continuous glucose monitoring (CGM) systems is described. Given the number of people that wear CGM systems and because CGM systems produce measurements continuously, a platform that provides a CGM system may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process. In implementations, a CGM platform includes a data analytics platform that obtains packages of glucose measurements provided by a CGM system and also obtains additional data associated with a user. The data analytics platform generates state information for the user by processing these CGM packages and the additional data, at least in part, by using one or more models. Based on this state information, the data analytics platform controls communication with the user, which may include generating intervention strategies to prevent users from transitioning to a negative state such as discontinuing use of the CGM system.

Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

The present disclosure provides a means for preventing or suppressing the leaching of a redox mediator constituting a reagent layer in a probe of an embedded biosensor, in particular, a means capable of improving preservation stability (durability) while maintaining glucose measurement sensitivity. The high molecular weight redox polymer according to the present disclosure is represented by general formula (A1), wherein X− represents an anionic species; L represents a linker; Poly represents a high molecular weight polymer; and R1 to R8 each independently represent a hydrogen atom or a substituent. The biosensor according to the present disclosure has a working electrode, a counter electrode, a reagent layer disposed on the working electrode, and a protective film covering at least the reagent layer, and the reagent layer contains an oxidoreductase that oxidizes or dehydrogenates the analyte and at least one high molecular weight redox polymer represented by general formula (A1).