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研究生:劉子華
研究生(外文):Tzu-Hua Liu
論文名稱:肝癌病患文字病歷報告之錯別字辨識與更正方法
論文名稱(外文):A method for detecting misspelled words in medical narrative reports: A case study on the patients with liver cancer
指導教授:賴飛羆賴飛羆引用關係
口試委員:鐘玉芳陳澤雄沈榮麟莊立民
口試日期:2012-06-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊網路與多媒體研究所
學門:電算機學門
學類:網路學類
論文種類:學術論文
論文出版年:2012
畢業學年度:101
語文別:英文
論文頁數:45
中文關鍵詞:病歷資料資訊擷取系統錯別字標準化醫療資訊
外文關鍵詞:textual medical recordsinformation extraction systemmisspellingnormalizationmedical informatics
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  • 被引用被引用:2
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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。然而,由於這些文字的醫療報告為醫護人員手動輸入,其中難免會有錯別字,這些因素造成醫療擷取系統抽取資訊上的困難,而因此遺漏掉無法被抽取出的珍貴醫療資訊,所以如何減少報告中錯別字對於未來的研究是非常重要的。
本研究的目的在於提供一個有效率的方式去辨識醫療報告中之錯別字並加以更正,以幫助醫療擷取系統能抽取出更多醫療資訊。我們設計一套錯別字辨正與標準化系統,用於校正報告中之錯別字。透過這套方法,能在系統抽取資訊前,將文章中內之錯別字更正,以幫助醫療擷取系統能從中擷取到先前因錯別字與不同表示法的原因而無法被擷取出來之資訊,以提高系統之資訊抽取率。由於醫療報告過於龐大,使用人為方式尋找錯誤是非常耗費人力與時間的,透過這個方法,可有效減少醫療報告之錯別字,並改善病歷之品質。


Textual medical records contain valuable information about diseases, medical procedures and treatment results. In our previous work, we implemented the information extraction system for extracting the desired information from liver cancer patients’ textual reports. These extracted results produced by information extraction system are used for supporting the development of recurrence predictive model. However, these narrative reports are made by human manually. Therefore, improving the correctness of medical reports is very important for further research.
In the study, we already implemented the information extraction system which can extract medical information for liver cancer recurrence predicting model. But, detecting and correcting the misspelling words of all medical reports 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 and correcting the misspelling words. We implemented the error handling system for correcting the misspelled words of each medical report. After the preprocessing procedure executed by the error handling system, the information extraction system can extract out those information which cannot be found due to the misspelling words. In this way, it can highly promote the accuracy of the medical extracted results.


口試委員會審定書 #
誌謝 i
中文摘要 iii
ABSTRACT iv
CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES ix
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Purpose 1
1.3 General Procedure 2
1.4 Thesis Organization 3
Chapter 2 Background 4
2.1 Electrical Clinical Report System 4
2.2 Information Extraction 4
2.2.1 Rule-Based Information Extraction System 5
2.2.2 Ontology 5
2.3 Information Extraction System on Medical Records 8
2.4 Liver Cancer 11
2.5 Methods of Diagnosis for Liver Cancer 11
2.5.1 Blood Test 11
2.5.2 Imaging Studies 12
2.5.3 Liver Biopsy 13
2.6 Treatment of Liver Cancer 13
2.6.1 Radiofrequency Ablation 13
2.6.2 Liver Resection 13
2.7 Datasets 14
2.7.1 Target Information 14
Chapter 3 Method 18
3.1 Information Extraction System 18
3.1.1 Regular Expression 19
3.1.2 Ontology 21
3.1.3 Concept Matching 24
3.2 Corpus 25
3.3 Approximate string matching algorithm 25
3.3.1 Soundex algorithm 26
3.3.2 PHONIX algorithm 27
3.3.3 Metaphone algorithm 29
3.3.4 Levenshtein Distance algorithm 30
3.4 Misspellings Handling System 34
Chapter 4 Results and Discussions 37
4.1 Evaluation Methods 37
4.2 Results 38
Chapter 5 Conclusions and Future Works 42
5.1 Conclusions 42
5.2 Future Works 42
References 44


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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.Wang, Z.-J., The methodology of facilitating data analysis in medical informatics -information extraction from free-text data and structural data collection through the structure report interface, in Graduate Institute of Biomedical Electronics and Bioinformatics 2010, National Taiwan University.
4.Spasic, I., et al., Text mining and ontologies in biomedicine: Making sense of raw text. Briefings in Bioinformatics, 2005. 6(3): p. 239-251.
5.McGuinness, D.L. and F.v. Harmelen. Available from: http://www.w3.org/TR/owl-features/.
6.Protege was developed by Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine. Available from: http://protege.stanford.edu/.
7.Wu, Y.-L., A method for identifying confidence level of the extracted results from medical narrative reports: A case study focus on the patients with liver cancer, master thesis, Graduate Institute of Biomedical Electronics and Bioinformatics 2012, National Taiwan University.
8.Mykowiecka, A., et al., Rule-based information extraction from patients'' clinical data. J. of Biomedical Informatics, 2009. 42(5): p. 923-936.
9.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.
10.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.
11.Keith E. Stuart, M. and M. Melissa Conrad Stoppler. Available from: http://www.medicinenet.com/liver_cancer/article.htm.
12.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.
13.Gadd, T.N., PHOENIX: the algorithm. Program: Autom. Libr. Inf. Syst., 1990. 24(4): p. 363-369.
14.Maynard, D.M.a.D., Metrics for Evaluation of Ontology-based Information, in In WWW 2006 Workshop on Evaluation of Ontologies for the Web2006.
15.Goyvaerts, J. 23 October 2011; Available from: http://www.regular-expressions.info/.
16.Martin, F.C., Approximate string matching algorithms in art media archives 2009, AGH University of Science and Technology.
17.Uzzaman, N., A Bangla Phonetic Encoding for Better Spelling Suggestion, in Proc. 7th International Conference on Computer and Information Technology2004.
18.Garaev, K.G., A Remark on the Bellman Principle of Optimality. Journal of The Franklin Institute, 1998. 335(2): p. 395-400.

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