公开(公告)号：US20240282462A1

公开(公告)日：2024-08-22

申请号：US18650699

申请日：2024-04-30

Applicant: Dexcom, Inc.

Inventor： Sarah Kate PICKUS ,  Brian BOBER

IPC: G16H50/70 ,  A61B5/00 ,  A61B5/145 ,  G16H10/60 ,  G16H40/67 ,  G16H50/20

CPC classification number: G16H50/70 ,  A61B5/14532 ,  A61B5/6801 ,  A61B5/7278 ,  A61B5/742 ,  A61B5/746 ,  G16H10/60 ,  G16H40/67 ,  G16H50/20

Abstract: 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.

公开(公告)号：US20230263479A1

公开(公告)日：2023-08-24

申请号：US18052194

申请日：2022-11-02

Applicant: Dexcom, Inc.

Inventor： Matthew Lawrence JOHNSON ,  Samuel Isaac EPSTEIN ,  Sarah Kate PICKUS ,  Lauren Hruby JEPSON ,  Kevin CHENG ,  Spencer Troy FRANK ,  Qi AN ,  Devon M. HEADEN ,  Abdulrahman JBAILY

IPC: A61B5/00 ,  A61B5/11 ,  G16H20/00 ,  A61B5/155 ,  A61B5/145

CPC classification number: A61B5/7275 ,  A61B5/1118 ,  G16H20/00 ,  A61B5/7203 ,  A61B5/155 ,  A61B5/14532

Abstract: Certain aspects of the present disclosure relate to methods and systems for predicting glycemic events in a patient induced as a result of physical activity. In certain aspects, a method includes monitoring a plurality of analytes of the patient continuously during a time period to obtain analyte data, the plurality of analytes including at least glucose and lactate. The method further includes processing the analyte data from the time period to determine an intensity level of physical activity engaged by the patient during the time period. The method further includes generating a glycemic event prediction using at least the analyte data for the plurality of analytes and the determination of physical activity intensity. The method further includes generating one or more recommendations for treatment for the patient based, at least in part, on the glycemic event prediction.