公开(公告)号：US20210338116A1

公开(公告)日：2021-11-04

申请号：US17114234

申请日：2020-12-07

Applicant: DexCom, Inc.

Inventor： Giada Acciaroli ,  Mark Derdzinski ,  Lauren Hruby Jepson ,  Andrew S. Parker

IPC: A61B5/145 ,  G06N3/08 ,  G16H40/67

Abstract: Hypoglycemic event prediction using machine learning is described. A CGM platform includes a machine learning model trained using historical time series glucose measurements of a user population. Once trained, the machine learning model predicts hypoglycemic events for users. When predicting hypoglycemic events, a time series of glucose measurements for a day time interval is received. The glucose measurements of this time series for the day time interval are provided by a CGM system worn by the user. The machine learning model predicts whether a hypoglycemic event will occur during a night time interval that is subsequent to the day time interval by processing the time series of glucose measurements using the trained machine learning model. The hypoglycemic event prediction is then output, such as via communication and/or display of a notification about the hypoglycemic event prediction.

公开(公告)号：US20210343402A1

公开(公告)日：2021-11-04

申请号：US17114106

申请日：2020-12-07

Applicant: DexCom, Inc.

Inventor： Giada Acciaroli ,  Mark Derdzinski ,  Lauren Hruby Jepson ,  Andrew S. Parker

IPC: G16H40/63 ,  G06N3/08

Abstract: Hypoglycemic event prediction using machine learning is described. A CGM platform includes a machine learning model trained using historical time series glucose measurements of a user population. Once trained, the machine learning model predicts hypoglycemic events for users. When predicting hypoglycemic events, a time series of glucose measurements for a day time interval is received. The glucose measurements of this time series for the day time interval are provided by a CGM system worn by the user. The machine learning model predicts whether a hypoglycemic event will occur during a night time interval that is subsequent to the day time interval by processing the time series of glucose measurements using the trained machine learning model. The hypoglycemic event prediction is then output, such as via communication and/or display of a notification about the hypoglycemic event prediction.