Medtronic Minimed, Inc.

University of Virginia Patent Foundation

Boyi Jiang

Yuxiang Zhong

Pratik J Agrawal

Huzefa F Neemuchwala

Sinu Bessy Abraham

Chantal M McMahon

Michael P Stone

Siddharth Arunachalam

Kevin E Velado

Anupam Phukan

Marc D Breton

Ali Dianaty

Kelly F Joy

Amit J Rushi

Chiara Fabris

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary method of querying a database involves receiving an input query from a client device, identifying a logical layer of a plurality of different logical layers of the database for searching based at least in part on the input query, generating a query statement for searching the identified logical layer of the plurality of different logical layers of the database based at least in part on the input query, querying the identified logical layer of the database using the query statement to obtain result data, and providing a search result influenced by the result data.

Patient data management systems and conversational interaction methods

Techniques for glucose level management are disclosed. In some examples, the techniques may involve obtaining an estimated protein value for a meal and determining an equivalent carbohydrate value for the meal based on the estimated protein value. In some examples, the techniques may involve obtaining an estimated fat value for a meal and determining, based on the estimated fat value, a glucose absorption rate resulting from consumption of the meal. In some examples, the equivalent carbohydrate value and/or the glucose absorption rate may be used to determine an amount of insulin to deliver to a patient to counteract a glucose level increase caused by consumption of the meal. The techniques may further involve generating an output indicative of the amount of insulin to deliver to the patient to counteract the glucose level increase caused by consumption of the meal.

Glucose level management based on fat content of meals

Glucose level management based on protein content of meals

Techniques for detection of occurrence of a meal are provided. In some embodiments, the techniques may involve obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval. The techniques may involve determining one or more glucose measurement statistics based on the plurality of glucose measurements. The techniques may involve determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics that are correlative to an occurrence of a meal for the patient. The techniques may involve detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.

Personalized event detection

A processor-implemented method includes causing fluid delivery to a patient based on a first value for a control parameter, identifying a second value for the control parameter based on a determination that a current operational context is associated with the second value, and causing fluid delivery to the patient based on the second value for the control parameter in lieu of the first value for the control parameter.

Contextual adjustment of control parameters

A processor-implemented method includes identifying a reference insulin absorption curve for a patient based on demographic data of the patient, identifying a contextual adjustment factor based on current contextual information for the patient, obtaining an adjusted insulin absorption curve based on the reference insulin absorption curve and the contextual adjustment factor, and causing insulin delivery to the patient based on the adjusted insulin absorption curve.

Contextual adjustment of insulin absorption curves

Disclosed herein are techniques related to personalized event detection. In some embodiments, the techniques may involve obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval, obtaining a patient-specific meal detection model that identifies one or more glucose measurement statistics that are correlative to occurrence of a meal for the patient, determining values for the one or more glucose measurement statistics based on the plurality of glucose measurements, determining a meal consumption metric based on the values for the one or more glucose measurement statistics, detecting an occurrence of the meal during the analysis interval based on the meal consumption metric, and in response to detecting the occurrence of the meal, providing an indication of the occurrence of the meal during with the analysis interval.

Disclosed herein are techniques related to glucose level management without carbohydrate counting. The techniques may involve obtaining contextual information for a meal, predicting amounts of glucose to be absorbed into a bloodstream of the patient over a duration of time due to consumption of the meal based on the contextual information for the meal, determining one or more amounts of insulin to counteract effects of the predicted amounts of glucose, and affecting insulin therapy based on outputting information indicative of the determined one or more amounts of insulin.

Glucose level management without carbohydrate counting

Infusion devices, systems and related methods of autonomously delivering fluid to a patient are disclosed. One method involves monitoring a current operational context during autonomous operation of the infusion device to deliver fluid to a patient in accordance with an operating mode using an initial value for a control parameter, and in response to identifying an adjusted value for the control parameter associated with the current operational context, autonomously operating the infusion device to deliver the fluid to the patient in accordance with the operating mode using the adjusted value for the control parameter in lieu of the initial value and thereafter reverting the control parameter to the initial value.

Contextual personalized closed-loop adjustment methods and systems

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary method of monitoring a physiological condition of a patient involves obtaining current measurement data for the physiological condition of the patient provided by a sensing arrangement, obtaining a user input indicative of one or more future events associated with the patient, and in response to the user input, determining a prediction of the physiological condition of the patient in the future based at least in part on the current measurement data and the one or more future events using one or more prediction models associated with the patient, and displaying a graphical representation of the prediction on a display device.

Patient-specific glucose prediction systems and methods

Infusion systems, infusion devices, and related operating methods are provided. A method of operating an infusion device involves determining a reference insulin absorption curve for the patient based at least in part on demographic data associated with the patient, determining a contextual adjustment factor based at least in part on a current patient context, calculating an adjusted insulin absorption curve for the patient as a function of the reference insulin absorption curve and the contextual adjustment factor, and determining a bolus dosage of the insulin based at least in part on the adjusted insulin absorption curve.

Infusion systems and related personalized bolusing methods

A computer-implemented system and related method of managing use of a diabetes management device are presented here. An embodiment of the method obtains input data for a user of the diabetes management device, and compares the input data against historical event/outcome combinations maintained for the user. Each of the event/outcome combinations includes insight event data indicative of a glycemic event and a glycemic outcome corresponding to the insight event data. The method determines, based on the comparing, a correlation between the input data and a glycemic outcome. The method continues by generating a glycemic insight message for delivery to the user, wherein the glycemic insight message includes information regarding a relationship between at least some of the input data and the glycemic outcome.

Data analytics and insight delivery for the management and control of diabetes

A computer-implemented system and related method of reporting glucose information for a user of a diabetes management device are presented here. An embodiment of the method obtains input data for the user of the diabetes management device, and identifies a tracked glycemic response event. One or more recommended glycemic control parameters are calculated based on an analysis of the obtained input data. The glycemic control parameters are calculated to extend a time period during which the user remains within a target glucose range following the tracked glycemic response event. The method continues by generating an output message that includes at least some of the recommended glycemic control parameters, and delivering the generated output message to a user device operated by the user of the diabetes management device.

Data analytics and generation of recommendations for controlling glycemic outcomes associated with tracked events

Medical devices and related patient management systems and event detection methods are provided. An exemplary method of detecting events pertaining to operation of a medical device, such as an infusion device, involves obtaining measurements indicative of a condition in a body of a patient, determining statistics for an analysis interval based on the measurements, and determining an event probability associated with the analysis interval based on historical event data associated with the patient. An event detection model associated with the patient is obtained and applied to the statistics and the event probability to identify occurrence of the event using the event detection model, and in response, an indication of the event associated with the analysis interval is provided, for example, by tagging or marking data in a database, displaying graphical indicia of the event, or the like.

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Northridge, CA, United States of America

Boyi Jiang