Hypoglycaemia Prediction Models With Auto Explanation

Abstract

World-wide statistics show a considerable growth of the occurrence of different types of Diabetes Mellitus, posing diverse challenges at many levels for public health policies. Some of these challenges may be addressed by means of computerised systems which may pave the way to provide practitioners with insight on their patient’s conditions anywhere and at anytime, but also to empower Diabetes patients as managers of their health. These systems for disease management come in many shapes and sizes, being the most promising trends the ones that involve expert systems that comprise specialised knowledge, use predictive models, feature engineering and reasoning. This study presents the state-of-the-art on reasoning and prediction models related with either blood glucose level or hypoglycaemia events. The main findings revealed are that there is room for improvement on predictive models, namely to enhance its accuracy and ability to forecast future events into a wider time frame. On the other hand, reasoning models are understudied and its usage in Diabetes management is reduced. We discuss an architecture that combines a predictive model and a reasoning system, with the objective of alerting of impending occurrences and interpret the current situation to accurately advise the diabetic user.

Keywords

• AI
• eHealth
• expert-systems
• data-mining
• prediction models
• diabetes mellitus