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

Abstract

Methods, systems, and devices for improving continuous glucose monitoring (“CGM”) are described herein. More particularly, the methods, systems, and devices describe retrieving a machine learning model that is trained to classify CGM sensor data and blanking the CGM sensor data based on an outlier classification from the machine learning model. The system may terminate sensors for which there is an aggregation of blanked CGM sensor data. The methods, systems, and devices described herein may additionally comprise a machine learning model that is trained to detect and correct for erroneous sensor use conditions based on error patterns in sensor data. The system may determine resolutions for correcting the detected erroneous sensor use conditions.

What is claimed is: 
     
         1 . A sensor device for applying machine learning models to improve integrated continuous glucose monitoring (“iCGM”) performance of continuous glucose monitoring (“CGM”) calibration algorithms, the sensor device comprising:
 memory configured to store a machine learning model, wherein the machine learning model is trained to identify outlier measurements from CGM sensor data based on sensor glucose-dependent performance against iCGM criteria using training data comprising clinical data on iCGM performance; and 
 a processor configured to:
 receive CGM sensor data; 
 input the sensor data in the machine learning model; 
 receive an output from the machine learning model indicating that the sensor data corresponds to an outlier measurement of the outlier measurements; and 
 blank the sensor data based on the output. 
 
 
     
     
         2 . The sensor device of  claim 1 , wherein the machine learning model is trained using a set of training sensor data that is labeled according to known classifications, the known classifications comprising a large negative bias, large positive bias, or nominal accuracy. 
     
     
         3 . The sensor device of  claim 1 , wherein the machine learning model is trained using a set of training sensor data that is labeled according to known classifications, the known classifications comprising poor accuracy, intermediate accuracy, or good accuracy. 
     
     
         4 . The sensor device of  claim 1 , wherein the sensor data comprises a current signal, a voltage signal, or impedance spectroscopy signals. 
     
     
         5 . The sensor device of  claim 4 , wherein, to input the sensor data in the machine learning model, the processor is further configured to generate a multi-dimensional feature input based on the sensor data. 
     
     
         6 . The sensor device of  claim 1 , wherein, to blank the sensor data based on the output, the processor is further configured to:
 determine a variable for a CGM calibration algorithm based on the output; and   determine whether to blank the sensor data based on the output from the machine learning model.   
     
     
         7 . The sensor device of  claim 1 , wherein the sensor data is received in first time intervals. 
     
     
         8 . The sensor device of  claim 1 , wherein the processor is further configured to:
 reset an outlier counter based on determining that a first sensor datapoint does not correspond to an outlier measurement;   cause the outlier counter to be increased based on determining that a second sensor datapoint corresponds to an outlier measurement;   compare the outlier counter to a threshold; and   terminate the sensor device based on determining that the outlier counter has breached the threshold.   
     
     
         9 . A method of applying machine learning models to improve integrated continuous glucose monitoring (“iCGM”) performance of continuous glucose monitoring (“CGM”) calibration algorithms by classifying outlier measurements based on iCGM criteria, the method comprising:
 receiving, at a sensor device, CGM sensor data; 
 inputting, at the sensor device, the sensor data in a machine learning model, wherein the machine learning model is trained to identify outlier measurements based on sensor glucose-dependent performance against iCGM criteria using training data comprising clinical data on iCGM performance; 
 receiving, at the sensor device, an output from the machine learning model indicating that the sensor data corresponds to an outlier measurement of the outlier measurements; and 
 blanking the sensor data based on the output. 
 
     
     
         10 . The method of  claim 9 , wherein the machine learning model is trained using a set of training sensor data that is labeled according to known classifications, the known classifications comprising a large negative bias, large positive bias, or nominal accuracy. 
     
     
         11 . The method of  claim 9 , wherein the machine learning model is trained using a set of training sensor data that is labeled according to known classifications, the known classifications comprising poor accuracy, intermediate accuracy, or good accuracy. 
     
     
         12 . The method of  claim 9 , wherein the sensor data comprises a current signal, a voltage signal, or impedance spectroscopy signals. 
     
     
         13 . The method of  claim 12 , wherein inputting the sensor data in the machine learning model comprises generating a multi-dimensional feature input based on the sensor data. 
     
     
         14 . The method of  claim 9 , wherein blanking the sensor data based on the output further comprises:
 determining a variable for a CGM calibration algorithm based on the output; and   determining whether to blank the sensor data based on the output from the machine learning model.   
     
     
         15 . The method of  claim 9 , wherein the sensor data is received in first time intervals. 
     
     
         16 . The method of  claim 9 , further comprising:
 resetting an outlier counter based on determining that a first sensor datapoint does not correspond to an outlier measurement;   causing the outlier counter to be increased based on determining that a second sensor datapoint corresponds to an outlier measurement;   comparing the outlier counter to a threshold; and   terminating the sensor device based on determining that the outlier counter has breached the threshold.   
     
     
         17 . 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 in a machine learning model, wherein the machine learning model is trained to identify outlier measurements based on sensor glucose-dependent performance against iCGM criteria using training data comprising clinical data on iCGM performance;   receiving, at the sensor device an output from the machine learning model indicating that the sensor data corresponds to an outlier measurement; and   blanking the sensor data based on the output.   
     
     
         18 . The non-transitory computer-readable media of  claim 17 , wherein the instructions cause further operations comprising:
 determining whether a set of training sensor data in the training data corresponds to known classification, wherein the known classification comprises a large negative bias, large positive bias, or nominal accuracy;   labeling the set of training sensor data with the known classification; and   training the machine learning model using the labeled set of training sensor data.   
     
     
         19 . The non-transitory computer-readable media of  claim 17 , wherein the instructions cause further operations comprising:
 determining whether a set of training sensor data in the training data corresponds to known classification, wherein the known classification comprises poor accuracy, intermediate accuracy, or good accuracy;   labeling the set of training sensor data with the known classification; and   training the machine learning model using the labeled set of training sensor data.   
     
     
         20 . The non-transitory computer-readable media of  claim 17 , wherein the instructions cause further operations comprising:
 resetting an outlier counter based on determining that a first sensor datapoint does not correspond to an outlier measurement;   causing the outlier counter to be increased based on determining that a second sensor datapoint corresponds to an outlier measurement;   comparing the outlier counter to a threshold; and   terminating the sensor device based on determining that the outlier counter has breached the threshold.