Machine learning models for detecting outliers and erroneous sensor use conditions and correcting, blanking, or terminating glucose sensors
Techniques for improving continuous glucose monitoring (“CGM”) are described herein. In some embodiments, the techniques involve obtaining sensor data; applying, to the sensor data, a machine learning model trained to identify sensor data error patterns; and detecting an erroneous sensor use condition based on output of the machine learning model indicating an error pattern identified in the sensor data.
1 . A system comprising:
one or more processors; and
one or more processor-readable media storing instructions which, when executed by the one or more processors, cause performance of:
obtaining sensor data from a glucose sensor inserted into subcutaneous tissue of a user;
identifying a sensor data error pattern in real time;
identifying an erroneous sensor use condition from two or more different erroneous sensor use conditions that each could cause the sensor data error pattern using a machine learning model;
in response to identifying the erroneous sensor use condition, predicting, using the machine learning model, a resolution from a plurality of possible resolutions that is most effective for resolving the erroneous sensor use condition, wherein the machine learning model was trained with training data comprising pairs of erroneous sensor use conditions and corresponding resolutions, wherein predicting the resolution from the plurality of possible resolutions comprises identifying a possible resolution that is most effective for resolving the erroneous sensor use condition among the plurality of possible resolutions as the predicted resolution;
causing implementation of the resolution;
causing delivery of insulin to the user by an insulin delivery device based on the sensor data manipulated by the implementation of the resolution; and
updating a configuration of the machine learning model based on the predicted resolution, an assessment of the predicted resolution, and reference feedback information, wherein the updated configuration is used for future predictions.
2 . The system of claim 1 , wherein the one or more processor-readable media further store instructions which, when executed by the one or more processors, cause performance of:
blanking display of the sensor data in response to identifying the sensor data error pattern by the machine learning model.
3 . The system of claim 1 , wherein the one or more processor-readable media further store instructions which, when executed by the one or more processors, cause performance of:
generating an alert informing of the erroneous sensor use condition in response to identifying the erroneous sensor use condition using the machine learning model.
4 . The system of claim 1 , wherein the implementation of the resolution comprises:
implementing the resolution by manipulating the sensor data via a user interface of a sensor device.
5 . The system of claim 1 , wherein:
the sensor data includes impedance spectroscopy signals measured at a plurality of signal frequencies using the glucose sensor; and
applying the machine learning model comprises generating, from the sensor data that includes the impedance spectroscopy signals, a multi-dimensional feature input to the machine learning model.
6 . The system of claim 1 , wherein the one or more processor-readable media further store instructions which, when executed by the one or more processors, cause performance of:
obtaining input indicating context information relating to the sensor data; and
applying the machine learning model to the context information relating to the sensor data, wherein outputs of the machine learning model are further based on the context information relating to the sensor data.
7 . The system of claim 6 , wherein the context information relating to the sensor data includes historic information relating to the sensor data over a time period.
8 . The system of claim 1 , wherein the machine learning model is trained based on clinical data on the erroneous sensor use condition.
9 . A processor-implemented method comprising:
obtaining sensor data from a glucose sensor inserted into subcutaneous tissue of a user;
identifying a sensor data error pattern in real time;
identifying an erroneous sensor use condition from two or more different erroneous sensor use conditions that each could cause the sensor data error pattern using a machine learning model;
in response to identifying the erroneous sensor use condition, predicting, using the machine learning model, a resolution from a plurality of possible resolutions that is most effective for resolving the erroneous sensor use condition, wherein the machine learning model was trained with training data comprising pairs of erroneous sensor use conditions and corresponding resolutions, wherein predicting the resolution from the plurality of possible resolutions comprises identifying a possible resolution that is most effective for resolving the erroneous sensor use condition among the plurality of possible resolutions as the predicted resolution;
causing implementation of the resolution;
causing delivery of insulin to the user by an insulin delivery device based on the sensor data manipulated by the implementation of the resolution; and
updating a configuration of the machine learning model based on the predicted resolution, an assessment of the predicted resolution, and reference feedback information, wherein the updated configuration is used for future predictions.
10 . The processor-implemented method of claim 9 , wherein the resolution comprises at least one of:
adjusting a signal of the sensor data;
adding a filter to a signal of the sensor data,
adjusting a filter of a signal of the sensor data,
replacing a signal of the sensor data;
removing a signal from the sensor data; or
replacing the glucose sensor with a replacement glucose sensor.
11 . The processor-implemented method of claim 9 , further comprising generating an alert informing of the erroneous sensor use condition in response to identifying the erroneous sensor use condition using the machine learning model.
12 . The processor-implemented method of claim 9 , wherein the implementation of the resolution comprises implementing the resolution by manipulating the sensor data via a user interface of a sensor device.
13 . The processor-implemented method of claim 9 , wherein:
the sensor data includes impedance spectroscopy signals measured at a plurality of signal frequencies using the glucose sensor; and
applying the machine learning model comprises generating, from the sensor data that includes the impedance spectroscopy signals, a multi-dimensional feature input to the machine learning model.
14 . The processor-implemented method of claim 9 , further comprising:
obtaining input indicating context information relating to the sensor data; and
applying the machine learning model to the context information relating to the sensor data, wherein outputs of the machine learning model are further based on the context information relating to the sensor data.
15 . The processor-implemented method of claim 14 , wherein the context information relating to the sensor data includes historic information relating to the sensor data over a time period.
16 . The processor-implemented method of claim 9 , wherein the machine learning model is trained based on clinical data on the erroneous sensor use condition.
17 . A processor-implemented method comprising:
obtaining sensor data from a glucose sensor inserted into subcutaneous tissue of a user, wherein the glucose sensor includes a single working electrode;
identifying a sensor data error pattern in real time;
identifying an erroneous sensor use condition from two or more different erroneous sensor use conditions that each could cause the sensor data error pattern using a machine learning model;
in response to identifying the erroneous sensor use condition, predicting, using the machine learning model, a resolution from a plurality of possible resolutions that is most effective for resolving the erroneous sensor use condition, wherein the machine learning model was trained with training data comprising pairs of erroneous sensor use conditions and corresponding resolutions, wherein predicting the resolution from the plurality of possible resolutions comprises identifying a possible resolution that is most effective for resolving the erroneous sensor use condition among the plurality of possible resolutions as the predicted resolution;
causing implementation of the resolution;
causing delivery of insulin to the user by an insulin delivery device based on the sensor data manipulated by the implementation of the resolution; and
updating a configuration of the machine learning model based on the predicted resolution, an assessment of the predicted resolution, and reference feedback information, wherein the updated configuration is used for future predictions.
18 . The processor-implemented method of claim 17 , further comprising:
obtaining input indicating context information relating to the sensor data,
wherein the sensor data error pattern comprises a low signal error pattern, and wherein the context information comprises historic sensor data over a time period that is used to disambiguate between the two or more different erroneous sensor use conditions comprising temporary signal loss and sensitivity loss.