COMPUTER-IMPLEMENTED METHOD FOR PREDICTING THE RISK OF A HYPOGLYCEMIC EVENT, DATA PROCESSING SYSTEM, COMPUTER PROGRAM, SYSTEM AND REMOTE CONTROL

Claims

A computer-implemented method for predicting the risk of a hypoglycemic event occurring during a prediction time interval is disclosed. The method comprises: providing user values ​​for a plurality of machine learning input parameters, wherein the user values ​​are determined from glucose monitoring data with glucose measurement values ​​detected for a user prior to a cutoff time, wherein the plurality of machine learning input parameters comprises: at least a first glucose measurement value determined from glucose measurement values ​​measured no more than 10 minutes prior to the cutoff time, and a hypoglycemic event proportion value indicating a proportion of prior prediction time intervals with detected hypoglycemic events out of a total number of prior prediction time intervals prior to the cutoff time;To predict, based on user values ​​and using a machine learning model, the risk of a hypoglycemic event occurring during a predicted time interval associated with the user's presumed sleep, wherein the machine learning model is generated based on machine learning input parameters and using historical glucose monitoring data from a user population; and to generate a result based on the predicted risk. A data processing system, a software program, a system, and a remote control are also disclosed.