Medtronic Minimed, Inc.

Huzefa F Neemuchwala

Yuxiang Zhong

Pratik J Agrawal

Chantal M McMahon

Boyi Jiang

Sinu Bessy Abraham

Michael P Stone

Anirban Roy

Benyamin Grosman

Louis J Lintereur

Patrick E Weydt

Ali Dianaty

Siddharth Arunachalam

John L Hoebing

Kevin E Velado

Anupam Phukan

Kelly F Joy

Amit J Rushi

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 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

Infusion devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary infusion device includes an actuation arrangement operable to deliver fluid to a user, a communications interface to receive measurement data indicative of a physiological condition of the user, a sensing arrangement to obtain contextual measurement data, and a control system coupled to the actuation arrangement, the communications interface and the sensing arrangement to determine a command for autonomously operating the actuation arrangement in a manner that is influenced by the measurement data and the contextual measurement data and autonomously operate the actuation arrangement in accordance with the command to deliver the fluid to the user.

Context-sensitive infusion devices, systems and methods

Disclosed herein are techniques related to dynamic forecasts. The techniques may involve obtaining, from a medical device, measurement data for a patient; forecasting a plurality of values for a condition of the patient based on a forecasting model and the obtained measurement data; providing a graphical user interface depicting the plurality of forecasted values and comprising a plurality of adjustable graphical user interface elements, each associated with a time period and an activity or event likely to influence the condition; obtaining an adjustment to a first adjustable graphical user interface element; in response to obtaining the adjustment to the first adjustable graphical user interface element, updating at least one of the forecasted values based on the adjustment to the first adjustable graphical user interface element, the obtained measurement data, and the forecasting model; and dynamically updating the graphical user interface to reflect the updated at least one forecasted value.

Dynamic forecasts

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.

A method for estimating glucose values of a user and an insulin infusion and management system are provided. The insulin infusion and management system can include an insulin infusion device configured to deliver insulin to a user; a blood glucose meter; a source of user activity data; and a processor-based computing device that supports data communication with the insulin infusion device. A processor device of the computing device can perform a method for estimating glucose values of a user. In accordance with the method, a first set of inputs can be received and processed via an estimation model for a user to generate a set of estimated glucose values that track actual glucose values.

Machine learning-based system for estimating glucose values based on blood glucose measurements and contextual activity data

Infusion systems, infusion devices, and related patient monitoring systems and methods are provided. A method of monitoring a physiological condition of a patient involves providing, on a display device, a graphical user interface display depicting forecast values with respect to different time periods in the future and including graphical user interface elements to allow a user to adjust a characteristic of an event likely influence the physiological condition of the patient at a respective time period. In response to an adjustment to a graphical user interface element, the method continues by dynamically updating the forecast values on the graphical user interface display using a forecasting model associated with the patient.

Patient day planning systems and methods

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 devices and related medical devices, patient data management systems, and methods are provided for monitoring a physiological condition of a patient. An exemplary database system includes a database to maintain data pertaining to a plurality of entities and a computing device coupled to the database to identify relationships between different entities of the plurality of entities, generate metadata defining a graph structure maintaining the relationships between the different entities of the plurality of entities for a plurality of different logical layers, and store the metadata in the database.

Patient data management systems and querying 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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Simi Valley, CA, United States of America

Huzefa F Neemuchwala