CPC A61B 5/14532 (2013.01) [A61B 5/7221 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 5/742 (2013.01); A61B 5/7475 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G16H 40/63 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01)] | 18 Claims |
1. A sensor device for applying micro machine learning models to reduce sensor glucose signal blanking, the sensor device comprising:
memory configured to store a plurality of machine learning models, wherein each machine learning model of the plurality of machine learning models is trained to predict a sensor glucose value under a particular outlier condition using training data comprising clinical data on the particular outlier condition; and
a processor configured to:
receive continuous glucose monitoring (CGM) sensor data;
identify a signature of input features in the CGM sensor data;
adjust weights of the plurality of machine learning models based on the signature of input features in the CGM sensor data;
receive an output from the plurality of machine learning models, the output indicating a predicted sensor glucose value based on weighting outputs of the plurality of machine learning models using the adjusted weights; and
cause displaying, on a display interface, the output indicating the predicted sensor glucose value such that the sensor glucose signal blanking is reduced.
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