ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements

Medical differential diagnosis (ddx) is based on the estimation of multiple distinct parameters in order to determine the most probable diagnosis. Building an intelligent medical differential diagnosis system implies using a number of knowledge-based technologies which avoid ambiguity, such as ontologies representing specific structured information, but also strategies such as computation of probabilities of various factors and logical inference, whose combination outperforms similar approaches. This paper presents ODDIN, an ontology-driven medical diagnosis system which applies the aforementioned strategies. The architecture and proof-of-concept implementation is described, and results of the evaluation are discussed.