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 rep resenting specific structured information, but also strategies such as computation of probabilities of var ious factors and logical inference, whose combination outperforms similar approaches. This paper presents ODDIN, an ontology driven medical diagnosis system which applies the aforementioned strat egies. The architecture and proof of concept implementation is described, and results of the evaluation are discussed.
ODDIN, an ontology-driven medical diagnosis system REF , uses a number of knowledge-based technologies to solve the problems of ambiguity which is very common in medical diagnosis, such as ontologies representing specific structure information and Computational and Mathematical Methods in Medicine 3 probabilistic statistical refinements.
ODDIN: Ontology-driven differential diagnosis based on logical inference and probabilistic refinements
Expert Syst. Appl.
Expert Syst. Appl.
Computer Science