Interactive Symptom Elicitation for Diagnostic Information Retrieval.

Medical information retrieval suffers from a dual problem: users struggle in describing what they are experiencing from a medical perspective and the search engine is struggling in retrieving the information exactly matching what users are experiencing. We demonstrate interactive symptom elicitation for diagnostic information retrieval. Interactive symptom elicitation builds a model from the user's initial description of the symptoms and interactively elicitates new information about symptoms by posing questions of related, but uncertain, symptoms for the user. As a result, the system interactively learns the estimates of symptoms while controlling the uncertainties related to the diagnostic process. The learned model is then used to rank the associated diagnoses that the user might be experiencing. Our preliminary experimental results show that interactive symptom elicitation can significantly improve user's capability to describe their symptoms, increase the confidence of the model, and enable effective diagnostic information retrieval.