SEPHYRES 1: A Physician Recommender System Based on Semantic Pain Descriptors and Multifaceted Reasoning

Physician recommender systems have emerged aimed at recommending the right physicians in accordance with patient preferences. However, such systems have been only based on techniques such as classification or syntax word-based search from previous patient recommendations and conditions with limited capabilities. In this paper, we propose a new model, we call SEPHYRES (semantic physician hybrid recommender expert system), through which we focus on the patient's medical conditions and pain description characteristics using an underlying evidence-based ontology. The ontology includes not only the semantic descriptions of the symptoms, but also the machine-understandable perceptions of the pain location and the link weights. In the proposed model, we applied a weight spreading pseudo-fuzzy method along with the general semantic reasoners with facets management module. To keep the domain manageable, we limited the scheme to diseases that cause abdominal pain. We used Harrison's Principles of Internal Medicine and Up-to-date online as our base evidence references along with the opinions from our local experts. We compared the results from our pseudo-diagnostic engine with twenty case studies from MEDSCAPE and PubMed databases. The results showed that our model can improve the machine awareness about the individual’s disease and thus improve the accuracy of recommendations.