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Categorization of electronic medical...
~
Zheng, Wanhong.
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Categorization of electronic medical news.
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Categorization of electronic medical news./
Author:
Zheng, Wanhong.
Description:
125 p.
Notes:
Advisers: Carolyn R. Watters; Evangelos E. Milios.
Contained By:
Masters Abstracts International40-05.
Subject:
Computer Science. -
Online resource:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ66610
ISBN:
0612666107
Categorization of electronic medical news.
Zheng, Wanhong.
Categorization of electronic medical news.
- 125 p.
Advisers: Carolyn R. Watters; Evangelos E. Milios.
Thesis (M.Comp.Sc.)--Dalhousie University (Canada), 2001.
This thesis presents a keyword-based medical newspaper article classification system. The system identifies the keywords that characterize the medical content of the article and classifies the article into four classes: non-medical, medical for layperson, medical for medicine-knowledgeable and medical for expert. Additionally, a classified medical article can be tagged with MeSH concepts for the purpose of content description.
ISBN: 0612666107Subjects--Topical Terms:
626642
Computer Science.
Categorization of electronic medical news.
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Categorization of electronic medical news.
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125 p.
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Advisers: Carolyn R. Watters; Evangelos E. Milios.
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Source: Masters Abstracts International, Volume: 40-05, page: 1264.
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Thesis (M.Comp.Sc.)--Dalhousie University (Canada), 2001.
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This thesis presents a keyword-based medical newspaper article classification system. The system identifies the keywords that characterize the medical content of the article and classifies the article into four classes: non-medical, medical for layperson, medical for medicine-knowledgeable and medical for expert. Additionally, a classified medical article can be tagged with MeSH concepts for the purpose of content description.
520
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In categorization core of the system, MeSH (Medical Subject Headings) hierarchical vocabulary is used as both a medical dictionary and categorization taxonomy. A manual pre-defined weighting scheme on MeSH concepts is used to differentiate medical keywords of different “difficulty” levels. Handcrafted classification algorithm has been investigated and the overall correctness is around 75%. Machine learning approach has been explored to construct 3-way classification rules automatically. While further tuning of medical training articles is proved to be necessary in order to get an effective classifier, the correctness on finding non-medical articles for our learning classifier is shown to be 92%.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ66610
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W9099906
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