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公开(公告)号：US20240350041A1
公开(公告)日：2024-10-24
申请号：US18760764
申请日：2024-07-01
Applicant: Dexcom, Inc.
Inventor： Mark Edward Selander , Alexander Michael Diener , Ryan Richard Ruehl , Kazanna Calais Hames , Mark Douglas Kempkey , Chad Michael Patterson , Apurv Ullas Kamath , Matthew Lawrence Johnson , Jason M. Halac , David A. Price , Peter C. Simpson , Devon M. Headen , Samuel Isaac Epstein
CPC classification number: A61B5/14546 , A61B5/1118 , A61B5/14532 , A61B5/165 , A61B5/7246 , A61B5/7275 , A61B5/7475 , G16H20/60
Abstract: Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.
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公开(公告)号：US20230129902A1
公开(公告)日：2023-04-27
申请号：US17971238
申请日：2022-10-21
Applicant: Dexcom, Inc.
Inventor： Jee Hye Park , Spencer Troy Frank , David A. Price , Kazanna C. Hames , Charles R. Stroyeck , Joseph J. Baker , Arunachalam Panch Santhanam , Peter C. Simpson , Abdulrahman Jbaily , Justin Yi-Kai Lee , Qi An
Abstract: Disease prediction using analyte measurements and machine learning is described. In one or more implementations, a combination of features of analyte measurements may be selected from a plurality of features of the analyte measurements based on a robustness metric and a performance metric of the combination, and a machine learning model may be trained to predict a health condition classification using the combination. The performance metric may be associated with an accuracy of predicting the health condition classification, and the robustness metric may be associated with an insensitivity to analyte sensor manufacturing variabilities on the accuracy. Once trained, the machine learning model predicts the health condition classification for a user based on analyte measurements of the user collected by a wearable analyte monitoring device. The combination of features may be extracted from the analyte measurements of the user and input into the machine learning model to predict the classification.
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公开(公告)号：US12048533B2
公开(公告)日：2024-07-30
申请号：US17188675
申请日：2021-03-01
Applicant: DexCom, Inc.
Inventor： Mark Edward Selander , Alexander Michael Diener , Ryan Richard Ruehl , Kazanna Calais Hames , Mark Douglas Kempkey , Chad Michael Patterson , Apurv Ullas Kamath , Matthew Lawrence Johnson , Jason M. Halac , David A. Price , Peter C. Simpson , Devon M. Headen , Samuel Isaac Epstein
CPC classification number: A61B5/14546 , A61B5/1118 , A61B5/14532 , A61B5/165 , A61B5/7246 , A61B5/7275 , A61B5/7475 , G16H20/60
Abstract: Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.
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公开(公告)号：US20210267506A1
公开(公告)日：2021-09-02
申请号：US17188675
申请日：2021-03-01
Applicant: DexCom, Inc.
Inventor： Mark Edward Selander , Alexander Michael Diener , Ryan Richard Ruehl , Kazanna Calais Hames , Mark Douglas Kempkey , Chad Michael Patterson , Apurv Ullas Kamath , Matthew Lawrence Johnson , Jason M. Halac , David A. Price , Peter C. Simpson , Devon M. Headen , Samuel Isaac Epstein
Abstract: Techniques for data analysis and user guidance are provided. One or more current measurements of one or more current analyte levels for the user are received from a sensor. A pattern is generated based on the one or more current measurements and the one or more past measurements. A first alignment with a first user target is then determined based on the pattern, where the first user target relates to one or more of a mental state or physical state of the user. A first result is output to the user, based on the determined first alignment.
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