Chronological Semantics Modeling: A Topic Evolution Approach in Online User-Generated Medical Data

Online medical discussion forums/question answering sites have become one of the major resources for people to look for healthcare information. These sites typically contain tremendous user-generated content (UGC) that possesses complex domain-specific information in layman's terms, which is the opposite of formal medical records kept in hospitals (i.e. Electronic Health Record). The goal of this project is to dissect semantics and extract valuable information systematically from UGC composed in unstructured and unconstrained format. We propose an automatic medical content analyzer that takes into account language semantics as well as progression (evolution) of medical events. The preliminary evaluation on the WebMD dataset shows that evolution-based recommendation uncovers broader domain semantic which might be ignored when using word-level or concept-based features.