A Context-Sensitive Support System for Medical Diagnosis Discovery based on Symptom Matching

Abstract

In this paper, we present a prototype of a clinical decision-support system. This prototype relies on a two-phase algorithm that is based on the differential diagnosis method from medical diagnostics and predictive models for disease occurrence in a subpopulation. The algorithm requires a data set containing information about diseases and their corresponding symptoms, and a data set with registered disease cases. The main output of this algorithm is a ranked list of diagnoses that might explain the manifested symptoms. The ranking is influenced by the patient's context, i.e., disease trends within a subpopulation to which the patient belongs. In the context of medical diagnosis discovery based on symptom matching, we present a short rationale for developing such a system, brief review of similar systems, algorithm for diagnoses ranking, and ideas for future research. Furthermore, we elaborate on the required data sets and illustrate the application of the proposed solution with a typical use scenario.