Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
    • 指導教授
  • 搜尋 TDR
  • 授權 Q&A
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 生醫電子與資訊學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6953
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor賴飛羆(Fei-pei Lai)
dc.contributor.authorYa-Lin Wuen
dc.contributor.author吳亞霖zh_TW
dc.date.accessioned2021-05-17T09:22:09Z-
dc.date.available2012-03-19
dc.date.available2021-05-17T09:22:09Z-
dc.date.copyright2012-03-19
dc.date.issued2012
dc.date.submitted2012-02-02
dc.identifier.citation1. Mamlin, B.W., D.T. Heinze, and C.J. McDonald, Automated extraction and normalization of findings from cancer-related free-text radiology reports. AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2003: p. 420-4.
2. Hripcsak, G., et al., Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing. Annals of Internal Medicine, 1995. 122(9): p. 681-688.
3. Hripcsak, G., et al., Use of Natural Language Processing to Translate Clinical Information from a Database of 889,921 Chest Radiographic Reports1. Radiology, 2002. 224(1): p. 157-163.
4. Chapman, W.W. and K.B. Cohen, Current issues in biomedical text mining and natural language processing. Journal of Biomedical Informatics, 2009. 42(5): p. 757-759.
5. Cohen, A.M. and W.R. Hersh, A survey of current work in biomedical text mining. Briefings in Bioinformatics, 2005. 6(1): p. 57-71.
6. Botsis, T., et al., Secondary Use of EHR: Data Quality Issues and Informatics Opportunities. AMIA Summits on Translational Science proceedings AMIA Summit on Translational Science, 2010. 2010: p. 1-5.
7. Keith E. Stuart, M. and M. Melissa Conrad Stoppler. Available from: http://www.medicinenet.com/liver_cancer/article.htm.
8. Myo Thant, M.; This content was last reviewed August 15, 2010 by Dr. Reshma L. Mahtani.]. Available from: http://www.caring4cancer.com/go/liver/basics.
9. Available from: http://www.faqs.org/health/topics/75/Liver-cancer.html.
10. Available from: http://cancerhelp.cancerresearchuk.org/type/liver-cancer/.
11. Wimalasuriya, D. and D. Dou, Ontology-based information extraction: An introduction and a survey of current approaches. Journal of Information Science, 2010. 36(3): p. 306-323.
12. Mykowiecka, A. and M. Marciniak, Domain model for medical information extraction-the lightmedont ontology, M. Marciniak and A. Mykowiecka, Editors. 2009. p. 333-357.
13. Mykowiecka, A., M. Marciniak, and A. Kupść, Rule-based information extraction from patients' clinical data. Journal of Biomedical Informatics, 2009. 42(5): p. 923-936.
14. Nassif, H., et al. Information Extraction for Clinical Data Mining: A Mammography Case Study. in Data Mining Workshops, 2009. ICDMW '09. IEEE International Conference on. 2009.
15. Boytcheva, S., et al., Integrating Patient-Related Entities Using Hospital Information System Data and Automatic Analysis of Free Text Availability, Reliability and Security for Business, Enterprise and Health Information Systems, A. Tjoa, et al., Editors. 2011, Springer Berlin / Heidelberg. p. 89-101.
16. Goyvaerts, J. 23 October 2011; Available from: http://www.regular-expressions.info/.
17. Spasic, I., et al., Text mining and ontologies in biomedicine: Making sense of raw text. Briefings in Bioinformatics, 2005. 6(3): p. 239-251.
18. McGuinness, D.L. and F.v. Harmelen. Available from: http://www.w3.org/TR/owl-features/.
19. ; Protege was developed by Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine.]. Available from: http://protege.stanford.edu/.
20. Chang, C.-C. and C.-J. Lin, LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2011. 2(3): p. 1-27.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/6953-
dc.description.abstract病歷資料擁有豐富的疾病、醫療程序和治療結果等資訊。在之前的研究裡,我們實做一資訊擷取系統提取肝癌病人文字報告裡肝癌相關資訊。資訊擷取系統提取的結果將用於建立預測肝癌復發的模型。資訊擷取後,重要的是證明這些提取結果是可靠的。但沒有經由人為檢查的方式,
在這項研究中,我們兩個團隊成員已檢查所有提取信息。根據檢查結果可得到資訊擷取系統的準確度。人為檢查所有提取結果的方式,是一個耗時耗力的工作。因此,本研究的目的在於提供一個有效率的方式去檢查提取結果。我們設計一驗證系統,用於預測每個提取結果的正確性。據驗證系統預測的結果,檢查人員可以有效地檢查那些被驗證系統預測為錯誤資訊的提取結果並且進行校正,而不需檢查所有的提取結果。透過驗證系統可以提高檢查提取結果的效率。
zh_TW
dc.description.abstractTextual medical records constitute a rich source of information about diseases, medical procedures and treatment results. In our previous work, we implemented the information extraction (IE) system for extracting the desired information from liver cancer patients’ textual reports. These extracted results produced by IE system are used for supporting the development of recurrence predictive model. After information was extracted by the IE system, it is important to prove these extracted results are reliable. However, we are not sure about the correctness of these extracted results without checking manually by the domain experts.
In the study, two of our team members had reviewed all extracted information. According to their reviews, the precision of the IE system can be analyzed. But, checking the correctness of all extracted results manually would be a time-consuming and labor-intensive task. Therefore, the aim of this study is to provide an efficient way for facilitating the process of checking all extracted results. We designed the validation system for predicting the correctness of each extracted result. According to the prediction of the validation system, the reviewers can efficiently check the smaller part of extracted results predicted as low confidence extracted information by the validation system and correct them; instead of checking all extracted information. In this way, it can highly promote the efficiency of the future reviewing process.
en
dc.description.provenanceMade available in DSpace on 2021-05-17T09:22:09Z (GMT). No. of bitstreams: 1
ntu-101-R98945039-1.pdf: 2641772 bytes, checksum: 3f3e84fa2add579be74d3c69723cc74d (MD5)
Previous issue date: 2012
en
dc.description.tableofcontents口試委員會審定書 #
誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Purpose 1
1.3 General Procedure 2
1.4 Thesis Organization 4
Chapter 2 Background 5
2.1 Information Extraction System on Medical Records 5
2.2 Liver Cancer 7
2.3 Diagnosis of Liver Cancer 7
2.3.1 Blood Test 7
2.3.2 Imaging Studies 8
2.3.3 Liver Biopsy 8
2.4 Treatment of Liver Cancer 9
2.4.1 Radiofrequency Ablation 9
2.4.2 Liver Resection 9
2.5 Related Works 9
2.6 Datasets 10
2.6.1 Target Information 11
Chapter 3 Method 15
3.1 Information Extraction System 15
3.1.1 Regular Expression 16
3.1.2 Ontology 17
3.1.3 Concept Matching 20
3.2 Human-Labeling Process 21
3.3 Validation System 26
Chapter 4 Results and Discussions 33
4.1 Results 33
4.2 Discussions 36
Chapter 5 Conclusions and Future Works 38
5.1 Conclusions 38
5.2 Future Works 38
References 41
dc.language.isoen
dc.title肝癌病患病歷報告之辨識資訊萃取結果可信賴程度zh_TW
dc.titleA method for identifying confidence level of the extracted results from medical narrative reports:
A case study focus on the patients with liver cancer
en
dc.typeThesis
dc.date.schoolyear100-1
dc.description.degree碩士
dc.contributor.oralexamcommittee李鴻璋(Hung-Chang Lee),尚榮基,莊仁輝(Jen-Hui Chuang),譚慶鼎(Ching-Ting Tan)
dc.subject.keyword病歷資料,資訊擷取系統,驗證系統,zh_TW
dc.subject.keywordTextual medical records,Information extraction system,Validation system,en
dc.relation.page42
dc.rights.note同意授權(全球公開)
dc.date.accepted2012-02-03
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept生醫電子與資訊學研究所zh_TW
顯示於系所單位:生醫電子與資訊學研究所

文件中的檔案:
檔案 大小格式 
ntu-101-1.pdf2.58 MBAdobe PDF檢視/開啟
顯示文件簡單紀錄


系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved