End of life detection for analyte sensors

A method for detecting outliers in data, comprising:

• generating sensor data comprising a signal from an analyte sensor at least partially implanted under a skin of a patient;
• evaluating a plurality of subsets of a calibration data set to determine a best subset;
• identifying a boundary associated with the best subset, wherein the boundary associated with the best subset is determined in real-time after the sensor is at least partially implanted under the skin of the patient;
• identifying at least one value outside the boundary as a possible outlier;
• evaluating a relevancy of the possible outlier to determine outlier information, wherein the evaluating includes receiving input from the patient indicative of a cause relating to the possible outlier, the patient input comprising at least one of:
• meal intake, insulin administration, or physical activity;
• performing processing responsive to the outlier information, wherein the processing includes modifying the best subset by removing the possible outlier based on the outlier information;
• calculating a value representative of an analyte concentration based on the best subset after the performing the processing responsive to the outlier information;
• and
• transmitting the value representative of the analyte concentration to a display.

A system comprising:

• a sensor configured to be at least partially disposed under a skin of a patient, the sensor configured to generate a signal;
• and
• at least one processor configured to be operably connected with the sensor, the processor configured to:
• evaluate a plurality of subsets of a calibration data set to determine a best subset;
• determine a boundary associated with the best subset, wherein the boundary associated with the best subset is determined after the sensor is disposed at least partially under the skin of the patient;
• determine at least one value outside the boundary as a possible outlier;
• receive input from the patient concerning the possible outlier, the input comprising at least one of:
• meal intake, insulin administration, or exercise;
• determine outlier information regarding the possible outlier based on the input received from the patient;
• transform the calibration data set responsive to the outlier information by removing the possible outlier in response to the determination of the outlier information;
• calculate a value representative of an analyte concentration based on the transformed calibration data set;
• and
• transmit the value representative of the analyte concentration to a display.