Continuous analyte monitor data recording device operable in a blinded mode

    公开(公告)号:US11510570B2

    公开(公告)日:2022-11-29

    申请号:US16922844

    申请日:2020-07-07

    Applicant: DexCom, Inc.

    Abstract: A system is provided for monitoring analyte in a host, including a continuous analyte sensor that produces a data stream indicative of a host's analyte concentration and a device that receives and records data from the data stream from the continuous analyte sensor. In one embodiment, the device includes a single point analyte monitor, from which it obtains an analyte value, and is configured to display only single point analyte measurement values, and not any analyte measurement values associated with data received from the continuous analyte sensor. Instead, data received from the continuous analyte sensor is used to provide alarms to the user when the analyte concentration and/or the rate of change of analyte concentration, as measured by the continuous analyte sensor, is above or below a predetermined range. Data received from the continuous analyte sensor may also be used to prompt the diabetic or caregiver to take certain actions, such as to perform another single point blood glucose measurement. In another embodiment, the device provides for toggling between two modes, with one mode that allows for display of glucose concentration values associated with the continuous glucose sensor and a second mode that prevents the display of glucose concentration values associated with the continuous glucose sensor.

    CONTINUOUS ANALYTE MONITOR DATA RECORDING DEVICE OPERABLE IN A BLINDED MODE
    8.
    发明申请
    CONTINUOUS ANALYTE MONITOR DATA RECORDING DEVICE OPERABLE IN A BLINDED MODE 有权
    连续分析监视数据记录装置可以以盲模式运行

    公开(公告)号:US20160058353A1

    公开(公告)日:2016-03-03

    申请号:US14938712

    申请日:2015-11-11

    Applicant: DexCom, Inc.

    Abstract: A system is provided for monitoring analyte in a host, including a continuous analyte sensor that produces a data stream indicative of a host's analyte concentration and a device that receives and records data from the data stream from the continuous analyte sensor. In one embodiment, the device includes a single point analyte monitor, from which it obtains an analyte value, and is configured to display only single point analyte measurement values, and not any analyte measurement values associated with data received from the continuous analyte sensor. Instead, data received from the continuous analyte sensor is used to provide alarms to the user when the analyte concentration and/or the rate of change of analyte concentration, as measured by the continuous analyte sensor, is above or below a predetermined range. Data received from the continuous analyte sensor may also be used to prompt the diabetic or caregiver to take certain actions, such as to perform another single point blood glucose measurement. In another embodiment, the device provides for toggling between two modes, with one mode that allows for display of glucose concentration values associated with the continuous glucose sensor and a second mode that prevents the display of glucose concentration values associated with the continuous glucose sensor.

    Abstract translation: 提供了一种用于监测主机中的分析物的系统,包括产生指示宿主分析物浓度的数据流的连续分析物传感器,以及从连续分析物传感器接收并记录来自数据流的数据的设备。 在一个实施例中,该装置包括单点分析物监测器,其从其获得分析物值,并且被配置为仅显示单点分析物测量值,而不是与从连续分析物传感器接收的数据相关联的任何分析物测量值。 相反,当通过连续分析物传感器测量的分析物浓度和/或分析物浓度的变化速率高于或低于预定范围时,来自连续分析物传感器的数据被用于向用户提供警报。 从连续分析物传感器接收的数据也可用于促使糖尿病患者或护理者采取某些措施,例如进行另一单点血糖测量。 在另一个实施例中,该装置提供两种模式之间的切换,其中一种模式允许显示与连续葡萄糖传感器相关联的葡萄糖浓度值,以及防止显示与连续葡萄糖传感器相关联的葡萄糖浓度值的第二模式。

    DIABETES PREDICTION USING GLUCOSE MEASUREMENTS AND MACHINE LEARNING

    公开(公告)号:US20220354395A1

    公开(公告)日:2022-11-10

    申请号:US17872823

    申请日:2022-07-25

    Applicant: DexCom, Inc.

    Abstract: Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.

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