E-health decision support system for differential diagnosis

Abstract

A new experimental system, capable to use the combined facilities offered by mobile communications, cloud computing and artificial intelligence, to assist the professional formation and specialization of medical staff and to offer up to date information for differential diagnosis, is proposed. To demonstrate the feasibility of the proposed approach, a proof-of-concept system was developed. An application in which two expert systems are used for the differential diagnosis of hypertension is presented. These systems aim to facilitate the diagnosis process of primary, endocrine and renal hypertension. A Naive Bayes Classifier and a Fuzzy Inference System were designed and implemented in order to differentiate the presented types of hypertension. The application was designed based on the client-server architecture, using Cloud Computing techniques and Android programming. The system take as inputs the preliminary medical information and investigation results that are sent from the Android client and outputs the precise risk of having a certain type of hypertension.