2026-09-20T20:23:43Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/24194 2023-07-10 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Schatz, Bruce R. Jiang, Yunliang 2011-05-25T14:51:43Z 2011-05-25T14:51:43Z 2011-05-25T14:51:43Z 2011-05 Case studies are a standard approach to medicine. A physician needs the outcomes of a drug, situationally relevant to a particular patient. We propose a system for patient outcomes utilizing computational semantics, which effectively digests message groups. Filtering identifies personal comments, by eliminating useless messages. Clustering groups similar topics from different messages, by statistical overlap with specified terms. Summarizing labels the clusters so content can be quickly digested. We implemented a prototype system with these functions for mining health messages. Our methods do not require extensive training or dictionaries, while enabling users to specify custom topics for digesting. This system has been tested with sample messages from our unique dataset from Yahoo! Groups, containing 12M personal messages from 27K public groups in Health and Wellness. Evaluated results show high quality of labeled clustering, promising an effective automatic system for discovering useful information across large volumes of health information. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-21T14:35:21Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 msthesis_yunliang.pdf: 181324 bytes, checksum: 153aba2511bca2a0858e080b50d55d85 (MD5) Jiang_Yunliang.pdf: 181324 bytes, checksum: 153aba2511bca2a0858e080b50d55d85 (MD5) Made available in DSpace on 2011-05-25T14:51:43Z (GMT). No. of bitstreams: 2 Jiang_Yunliang.pdf: 181324 bytes, checksum: 153aba2511bca2a0858e080b50d55d85 (MD5) license.txt: 4062 bytes, checksum: d4e0724185ccc2e2253c3a40a8aa6150 (MD5) http://hdl.handle.net/2142/24194 en Copyright 2011 Yunliang Jiang drug-based outcome filtering clustering summarizing A computational semantics system for detecting drug reactions and patient outcomes in personal health messages Computer Science 1434 Computer Science 0112 University of Illinois at Urbana-Champaign Thesis M.S. PHD:Computer Science -UIUC 10KS0112PHD