An Adaptive Expert System for Automated Advices Generation-Based Semi-continuous M-Health Monitoring

Abstract

Chronic diseases such as diabetes and hypertension have been recognized in the last decade among the principal causes of death in the world. Mitigating and controlling the elicited risks necessitate a continuous monitoring to produce accurate recommendations for both patients and physicians. For patient, it will help in adjusting his/her lifestyles, medications, and sport activities. However, for physicians, it helps in taking guided therapy decision. In this paper, we propose an adaptive Expert System (ES) that relies, not only on a set of rules validated by experts, but also linked to an intelligent continuous monitoring scheme that copes with semi-continuous data streams by implementing smart sensing and pre-processing of data. In addition, we implemented an iterative data analytic technique that learns from the past ES experience to continuously improve clinical decision-making and automatically generates validated advices. These advices are visualized via an application interface. We experimented the proposed system using different scenarios of monitoring blood sugar and blood pressure parameters of a population of patients with chronic diseases. The results we have obtained showed that our ES combined with the intelligent monitoring and analytic techniques provide a high accuracy of collected data and evident-based advices.