Machine learning models for detecting outliers and erroneous sensor use conditions and correcting, blanking, or terminating glucose sensors

A method of applying machine learning models to identify erroneous sensor use conditions in CGM sensor data, the method comprising:

• receiving, at a sensor device, CGM sensor data;
• inputting, at the sensor device the sensor data into a machine learning model, wherein the machine learning model is trained to identify erroneous sensor use conditions based sensor data error patterns using training data comprising clinical data on erroneous sensor use conditions;
• and
• receiving, at the sensor device, an output from the machine learning model indicating an erroneous sensor use condition based on an error pattern identified in the sensor data.

A non-transitory computer-readable media for continuous glucose monitoring comprising instructions that, when executed by one or more processors, cause operations comprising:

• receiving, at a sensor device, CGM sensor data;
• inputting, at the sensor device the sensor data into a machine learning model, wherein the machine learning model is trained to identify erroneous sensor use conditions based sensor data error patterns using training data comprising clinical data on erroneous sensor use conditions;
• and
• receiving, at the sensor device, an output from the machine learning model indicating an erroneous sensor use condition based on an error pattern identified in the sensor data.