Exploring Clustering Based Knowledge Discovery towards Improved Medical Diagnosis

We propose to develop a framework for an intelligent reasoner with capabilities that support complex decision making processes in medical diagnosis. Identifying the causes, reasoning the effects to explore information geometry and learning the associated factors, from medical forum information extracted, are the core aspects of this work. As part of the proposed framework, we present an approach that identifies semantically similar causes and effects for any specific disease from medical diagnosis literature using implicit semantic interconnections among the medical terms. First we crawled MedHelp forum data and considered two types of information: forums data and posts data. Each forum link points to a specific disease and consists of several topics pertaining to that disease. Each topic consists of multiple posts that carry either users’ queries/difficulties or doctor’s feedback pertaining to the issue(s) of the users. We use graph based exploration on the terms (diseases) and their relations (in terms of causes/effects) and explore the information geometry pertaining to similar diseases. We performed a systematic evaluation to identify the relevance of the contextual information retrieved for a specific disease and similar factors across different diseases. The proposed approach looks promising in capturing similar causes and/or effects that pertain to multiple diseases. This would enable medical practitioners to have a multi-faceted view of a specific disease/condition.