Semantic tagging for medical documents using a hidden markov model

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We propose a semantic tagger that provides high level concept information for phrases based on several kinds of low level information about words in clinical narrative texts. It delineates such information from the statements written by doctors in patient records. The tagging, based on Hidden Markov Model (HMM), is performed on the text that have been tagged with Unified Medical Language System (UMLS), Part-of-Speech (POS), abbreviation tags, and numeric tags. It reuses UMLS, POS, abbreviation, clue words, and numerical information to produce higher level concept information. Our unknown phrase guessing method for a robust tagger also uses the existing information calculated in the training data. In short, the semantic tagger gives more meaningful and abstract information by integrating different kinds of low-level information. The result can be used to extract clinical knowledge that can support decision making or quality assurance of medical treatment.
Advisors
Myaeng, Sung-Hyonresearcher맹성현researcher
Description
한국정보통신대학교 : 공학부,
Publisher
한국정보통신대학교
Issue Date
2006
Identifier
392713/225023 / 020044568
Language
eng
Description

학위논문(석사) - 한국정보통신대학교 : 공학부, 2006.8, [ vi, 49 p. ]

Keywords

의료 문서; 시맨틱 태깅; HMM; Information System; Medical; Semantic Tagging; Hidden Markov Model

URI
http://hdl.handle.net/10203/55505
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=392713&flag=dissertation
Appears in Collection
School of Engineering-Theses_Master(공학부 석사논문)
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