GLUCOSE ALERT PREDICTION HORIZON MODIFICATION

    公开(公告)号:US20220061775A1

    公开(公告)日:2022-03-03

    申请号:US17464456

    申请日:2021-09-01

    Applicant: DexCom, Inc.

    Abstract: Data describing glucose measurements is received from a continuous glucose monitoring (CGM) system worn by a user and predicted glucose values during a future time period are generated for the user based on the data. A determination is made that at least one of the predicted glucose values satisfies a threshold value for an alert, which is associated with a prediction horizon that defines an amount of time prior to satisfaction of the threshold value for communicating the alert to the user. Output of the alert is caused responsive to determining that the at least one predicted glucose value satisfies the threshold value for the alert within the prediction horizon, relative to a current time. The prediction horizon is modified based on a user response to the alert. Output of a subsequent instance of the alert is caused based on the modified prediction horizon.

    GLUCOSE ALERT PREDICTION HORIZON MODIFICATION

    公开(公告)号:US20220061712A1

    公开(公告)日:2022-03-03

    申请号:US17464447

    申请日:2021-09-01

    Applicant: DexCom, Inc.

    Abstract: Data describing glucose measurements is received from a continuous glucose monitoring (CGM) system worn by a user and predicted glucose values during a future time period are generated for the user based on the data. A determination is made that at least one of the predicted glucose values satisfies a threshold value for an alert, which is associated with a prediction horizon that defines an amount of time prior to satisfaction of the threshold value for communicating the alert to the user. Output of the alert is caused responsive to determining that the at least one predicted glucose value satisfies the threshold value for the alert within the prediction horizon, relative to a current time. The prediction horizon is modified based on a user response to the alert. Output of a subsequent instance of the alert is caused based on the modified prediction horizon.

    GLUCOSE MEASUREMENT PREDICTIONS USING STACKED MACHINE LEARNING MODELS

    公开(公告)号:US20210378563A1

    公开(公告)日:2021-12-09

    申请号:US17334448

    申请日:2021-05-28

    Applicant: DexCom, Inc.

    Abstract: Glucose measurement and glucose-impacting event prediction using a stack of machine learning models is described. A CGM platform includes stacked machine learning models, such that an output generated by one of the machine learning models can be provided as input to another one of the machine learning models. The multiple machine learning models include at least one model trained to generate a glucose measurement prediction and another model trained to generate an event prediction, for an upcoming time interval. Each of the stacked machine learning models is configured to generate its respective output when provided as input at least one of glucose measurements provided by a CGM system worn by the user or additional data describing user behavior or other aspects that impact a person's glucose in the future. Predictions may then be output, such as via communication and/or display of a notification about the corresponding prediction.

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