Abstract
We propose a semantic tagger that provides high level concept information for phrases in clinical documents. 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 documents that have been tagged with Unified Medical Language System (UMLS), Part-of-Speech (POS), and abbreviation tags. The result can be used to extract clinical knowledge that can support decision making or quality assurance of medical treatment.
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© 2006 Springer-Verlag Berlin Heidelberg
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Jang, H., Song, S.K., Myaeng, S.H. (2006). Text Mining for Medical Documents Using a Hidden Markov Model. In: Ng, H.T., Leong, MK., Kan, MY., Ji, D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880592_45
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DOI: https://doi.org/10.1007/11880592_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45780-0
Online ISBN: 978-3-540-46237-8
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