A hybrid reasoning system for mobile and intelligent health services

Recently, innovative and mobile health services have been developed by embedding knowledge-based systems, with the aim of remotely promoting wellness and healthy lifestyle, monitoring patients' chronic diseases and improving their adherence to therapies. Even if different knowledge-based systems have been proposed for mobile devices, they are typically based on precise production rules built on the top of ontological primitives for describing the domain of interest. Thus, they are not able to handle medical knowledge graded and affected by uncertainty, which often underlies medical decision-making processes. In order to address this topic, this paper presents a hybrid, rule-based reasoning system for mobile devices aimed at enabling the realization of intelligent health services. This system is essentially characterized by two main features: i) a hybrid knowledge representation approach for modelling productions rules involving both precise and vague information by integrating ontological and fuzzy primitives; ii) a lazy reasoning algorithm able to efficiently process this hybrid knowledge and timely produce answers. A case study has been arranged in order to evaluate the effectiveness of the proposed system within a mobile application for detecting heart arrhythmias.