MACHINE LEARNING MODELS FOR DETECTING OUTLIERS AND ERRONEOUS SENSOR USE CONDITIONS AND CORRECTING, BLANKING, OR TERMINATING GLUCOSE SENSORS

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...

Full description

Saved in:
Bibliographic Details
Main Authors NOGUEIRA, KEITH, AJEMBA, PETER, VARSAVSKY, ANDREA, GEE, ELAINE, NISHIDA, JEFFREY
Format Patent
LanguageEnglish
Published 16.06.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
PHYSICS
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:Application Number: US202017121624