Virtual cardiologist — A conversational system for medical diagnosis

In this paper we describe a very preliminary conversational system focused on the automated diagnosis of heart conditions. The system uses a relational Ngram model to extract meaning from user queries and answers, and maintains a belief network consisting of 48 different cardiac conditions, symptoms, and other internal system parameters. The end result is a system that can process an initial user query, ask pertinent questions, and to deduce reasonable medical conclusions. Although several example output conversations are shown, the medical accuracy of the diagnosis is not the focus of this paper. Instead this paper focuses on the system level infrastructure and methodology for building a medical conversation system.