title: Designing and evaluating a clustering system for organizing and integrating patient drug outcomes in personal health messages.
abstract: Patient outcomes to drugs vary, but physicians currently have little data about individual responses. We designed a comprehensive system to organize and integrate patient outcomes utilizing semantic analysis, which groups large collections of personal comments into a series of topics. A prototype implementation was built to extract situational evidences by filtering and digesting user comments provided by patients. Our methods do not require extensive training or dictionaries, while categorizing comments based on expert opinions from standard source, or patient-specified categories. This system has been tested with sample health messages from our unique dataset from Yahoo! Groups, containing 12M personal messages from 27K public groups in Health and Wellness. We have performed an extensive evaluation of the clustering results with medical students. Evaluated results show high quality of labeled clustering, promising an effective automatic system for discovering patient outcomes from large volumes of health information.
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA.
authors.forename: Yunliang
authors.initials: Y
authors.lastname: Jiang
authors.forename: Qingzi Vera
authors.initials: QV
authors.lastname: Liao
authors.forename: Qian
authors.initials: Q
authors.lastname: Cheng
authors.forename: Richard B
authors.initials: RB
authors.lastname: Berlin
authors.forename: Bruce R
authors.initials: BR
authors.lastname: Schatz
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems; D016000:Cluster Analysis; D057225:Data Mining; D004358:Drug Therapy; D001330:Electronic Data Processing; D006266:Health Education; D006801:Humans; D055641:Mathematical Concepts; D017063:Outcome Assessment, Health Care / Q000379:methods*; D039781:PubMed; D060388:Support Vector Machine; D009626:Terminology as Topic