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研究生:顧憶珍
研究生(外文):Yi-Chen Ku
論文名稱:自動處理病理報告以分類腦瘤個案
論文名稱(外文):Automatic Processing of Pathological Reports for Classification of Brain Tumors
指導教授:莊人祥莊人祥引用關係
指導教授(外文):Jen-Hsiang Chuang
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:衛生資訊與決策研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:71
中文關鍵詞:樣式相配規則文字分類自然語言處理腦瘤
外文關鍵詞:Pattern-matching ruleText classificationNatural Language ProcessingBrain tumor
相關次數:
  • 被引用被引用:1
  • 點閱點閱:148
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
腦瘤種類超過120種以上,使得診斷和治療變為複雜。腦瘤的分類不僅可以幫助醫生釐清臨床上的診斷及治療,對教學研究也很重要。
本研究根據世界衛生組織在2000年頒布的腦瘤分類國際標準,發展出一套自動分類系統,名為Brain-Tumor Classifier,用於腦瘤病歷的分類.本研究使用自然語言處理中的樣式相配規則 (pattern-matching rules) 技術,經分析846筆腦瘤病理報告以及與專家討論後,製定九種規則,配合病理報告中的關鍵字(keyword)進行腦瘤分類,再使用276筆測試資料(Testing set),依據三位兒童神經外科醫生製訂的黃金標準進行測試。
使用Brain-Tumor Classifier來分類,經依黃金標準測試後,Brain-Tumor Classifier與三位醫生分類的結果,在統計上無顯著差異。本Brain-Tumor Classifier的正確率達91%,敏感度為90.83%,精確度為99.74%,陽性預測值為91.67%。
本研究根據世界衛生組織在2000年頒布的腦瘤分類國際標準,提出了九種樣式相配規則及關鍵字,可處理分析病理報告做腦瘤疾病的自動分類,結果顯示不錯的可行性。
There are over 120 different types of brain tumors, making effective treatment very complicated. Classification of brain tumors accurately can not only help the doctors to treat the patients correctly but also help doctors to do research and teaching in this field efficiently. The objective of our study was to classify pathological reports into different classes of brain tumors automatically according to World Health Organization 2000 classification of brain tumors. We developed pattern-matching rules called Brain-Tumor Classifier processing pathological reports and classifying brain tumors automatically. We compared Brain-Tumor Classifier against a gold standard that was established by three experts judging 276 records. In this testing set, Brain-Tumor Classifier had a specificity of 99.74% (versus 99.79 ~ 99.9 % for the physicians), a positive predictive value of 91.67% (versus 82.35 ~ 94.92 % for the physicians) while maintaining a reasonable sensitivity of 90.83% (versus 85.91 ~ 97.93 % for the physicians). In addition, it had accuracy of 91.1%. We conclude that automatic processing of pathological reports for classification of brain tumors is feasible and useful.
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