Medical-treatment Recommendation and the Integration of Process Models into Knowledge-based Systems

Abstract

Decision making based on evidence other than human reasoning is becoming increasingly important in healthcare. Valuable evidence is in the form of treatment processes used by healthcare institutions and this paper presents a new framework for representing and modeling knowledge from these processes. Specifically, it presents the integration of data from literature, business processes and decision trees through workflows that cover the full cycle of health care, from diagnosis to prognosis and treatment. With respect to patient status, as single instants cannot convey sufficient information, time series are analyzed and classified to improve decision-making ability. The elicitation of new knowledge takes into account international standards, ontologies, information models, nomenclatures and multiple types of indicators. The integration of formal process-modeling in knowledge-based systems is exemplified by a real-world recommendation scenario. After evaluation with a medical-rehabilitation data set, results show a strong correspondence between treatment recommended by the proposed system and clinical practice.