CLUSTERING AND COMPARING INFORMATION EXTRACTED FROM PERSONAL HEALTH MESSAGES BY

The development of Web 2.0 techniques has led to the prosperity of online communities, which spread to various domains and areas in our daily life. When it comes to the medicine and healthcare domain, a series of good online services such as Yahoo! Groups, WebMD and Med-Help, offer patients and physicians a good platform to discuss health problems, e.g., diseases and drugs, diagnoses and treatments, which also provide a large volume of data for researchers to analyze and explore. However, some nature of the personal messages, e.g., unclean, unstruc-tured and isolated from clinical practice, hinders users ’ effective digestion of information in the front end and challenges the data analysis in the back end. In such a scenario, the objective of my thesis is to apply the advanced data mining, information retrieval and natural language processing techniques to effectively analyze and re-organize the rich source of personal health messages from online medical communities, in order to satisfy patients ’ information need and support physicians ’ clinical practice. Specially, in the first part of the dissertation, I introduce an SVM-based multi-class clas-sification method which utilizes term-appearance, lexical and semantic features to effectivel