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電子病歷命名實體識別和實體關(guān)系抽取研究綜述

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  本文關(guān)鍵詞:電子病歷命名實體識別和實體關(guān)系抽取研究綜述,由筆耕文化傳播整理發(fā)布。


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電子病歷(Electronic medical records,EMR)產(chǎn)生于臨床治療過程,其中命名實體和實體關(guān)系反映了患者健康狀況,包含了大量與患者健康狀況密切相關(guān)的醫(yī)療知識,因而對它們的識別和抽取是信息抽取研究在醫(yī)療領(lǐng)域的重要擴(kuò)展. 本文首先討論了電子病歷文本的語言特點和結(jié)構(gòu)特點,然后在梳理了命名實體識別和實體關(guān)系抽取研究一般思路的基礎(chǔ)上,分析了電子病歷命名實體識別、實體修飾識別和實體關(guān)系抽取研究的具體任務(wù)和對應(yīng)任務(wù)的主要研究方法. 本文還介紹了相關(guān)的共享評測任務(wù)和標(biāo)注語料庫以及醫(yī)療領(lǐng)域幾個重要的詞典和知識庫等資源. 最后對這一研究領(lǐng)域仍需解決的問題和未來的發(fā)展方向作了展望.

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收稿日期: 2013-08-30     

基金資助:

國家自然科學(xué)基金(60975077)資助

通訊作者: 關(guān)毅 哈爾濱工業(yè)大學(xué)教授. 主要研究方向為智能信息檢索,網(wǎng)絡(luò)挖掘,自然語言處理,,認(rèn)知語言學(xué).E-mail:guanyi@hit.edu.cn     E-mail: guanyi@hit.edu.cn

作者簡介: 楊錦鋒 哈爾濱工業(yè)大學(xué)博士研究生.主要研究方向為自然語言處理,電子病歷信息抽取.E-mail:yangjinfeng2010@gmail.com

引用本文:   

楊錦鋒, 于秋濱, 關(guān)毅, 蔣志鵬. 電子病歷命名實體識別和實體關(guān)系抽取研究綜述. 自動化學(xué)報, 2014, 40(8): 1537-1562.
Yang Jin-Feng, YU Qiu-Bin, GUAN Yi, JIANG Zhi-Peng. An Overview of Research on Electronic Medical Record Oriented Named Entity Recognition and Entity Relation Extraction. Acta Automatica Sinica, 2014, 40(8): 1537-1562.

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[1] Ministry of Health of the People's Republic of China. The basic specifications of electronic medical records (trial) [Online], available: , December 27, 2013(中華人民共和國衛(wèi)生部. 電子病歷基本規(guī)范(試行) [Online], available: 547432.htm, December 27, 2013)
[2] Wasserman R C. Electronic medical records (EMRs), epidemiology, and epistemology: reflections on EMRs and future pediatric clinical research. Academic Pediatrics, 2011, 11(4): 280-287
[3] Uzuner O, Mailoa J, Ryan R, Sibanda T. Semantic relations for problem-oriented medical records. Artificial Intelligence in Medicine, 2010, 50(2): 63-73
[4] Demner-Fushman D, Chapman W W, McDonald C J. What can natural language processing do for clinical decision support? Journal of Biomedical Informatics, 2009, 42(5): 760-772
[5] Eysenbach G. Recent advances: consumer health informatics. British Medical Journal, 2000, 320(7251): 1713-1716
[6] Lin Dong, Shao Jun-Li. A general and practical diagnosing and treating expert system of medicine. Acta Automatica Sinica, 1995, 21(3): 380-382(林東, 邵軍力. 醫(yī)學(xué)診療領(lǐng)域通用專家系統(tǒng)設(shè)計與實現(xiàn). 自動化學(xué)報, 1995, 21(3): 380-382)
[7] Sager N, Friedman C, Lyman M S. Review of Medical language processing: computer management of narrative data. Computational Linguistics, 1989, 15(3): 195-198
[8] National Institutes of Health. Research Repositories, Databases, and the HIPAA Privacy Rule [Online], available: , December 27, 2013
[9] Uzuner O, Luo Y, Szolovits P. Evaluating the state-of-the-art in automatic de-identification. Journal of the American Medical Informatics Association, 2007, 14(5): 550-563
[10] Uzuner O, Solti I, Cadag E. Extracting medication information from clinical text. Journal of the American Medical Informatics Association, 2010, 17(5): 514-518
[11] Xu Yong-Dong, Quan Guang-Ri, Wang Ya-Dong. Research of electronic medical record key information extraction based on HL7. Journal of Harbin Institute of Technology, 2011, 43(11): 89-94(徐永東, 權(quán)光日, 王亞東. 基于HL7的電子病歷關(guān)鍵信息抽取技術(shù)研究. 哈爾濱工業(yè)大學(xué)學(xué)報, 2011, 43(11): 89-94)
[12] Uzuner O, South B R, Shen S, DuVall S L. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Association, 2011, 18(5): 552-556
[13] Chapman W W, Bridewell W, Hanbury P, Cooper G F, Buchanan B G. A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics, 2001, 34(5): 301-310
[14] Zheng J P, Chapman W W, Crowley R S, Savova G K. Coreference resolution: a review of general methodologies and applications in the clinical domain. Journal of Biomedical Informatics, 2011, 44(6): 1113-1122
[15] Tian Y H. Coreference Resolutionon Entities and Events for Hospital Discharge Summaries [Master dissertation], Massachusetts Institute of Technology, USA, 2007
[16] Uzuner O, Bodnari A, Shen S Y, Forbush T, Pestian J, South B R. Evaluating the state of the art in coreference resolution for electronic medical records. Journal of the American Medical Informatics Association, 2012, 19(5): 786-791
[17] Filannino M. Temporal expression normalisation in natural language texts. ArXiv Preprint, ArXiv Preprint arXiv: 1206.2010, 2012
[18] UzZaman N, Llorens H, Allen J, Derczynski L, Verhagen M, Pustejovsky J. TempEval-3: Evaluating events, time expressions, and tem-poral relations. ArXiv Preprint, ArXiv Preprint arXiv: 1206.5333, 2012
[19] Zhou X J, Li H M, Lu X D, Duan H L. Temporal expression recognition and temporal relationship extraction from Chinese narrative medical records. In: Proceedings of the 5th International Conference on Bioinformatics and Biomedical Engineering. Wuhan, China: IEEE, 2011. 1-4
[20] Sun W, Rumshisky A, Uzuner O. Evaluating temporal relations in clinical text: 2012 I2B2 challenge. Journal of the American Medical Informatics Association, 2013, 20(5): 806-813
[21] Tange H J, Hasman A, Robbe P F, Schouten H C. Medical narratives in electronic medical records. International Journal of Medical Informatics, 1997, 46(1): 7-29
[22] McDonald C J, Overhage J M, Tierney W M, Dexter P R, Martin D K, Suico J G, Zafar A, Schadow G, Blevins L, Glazener T, Meeks-Johnson J, Lemmon L, Warvel J, Porterfield B, Warvel J, Cassidy P, Lindbergh D, Belsito A, Tucker M, Williams B, Wodniak C. The regenstrief medical record system: a quarter century experience. International Journal of Medical Informatics, 1999, 54(3): 225-53
[23] Fries J F. Time-oriented patient records and a computer databank. Journal of the American Medical Association, 1972, 222(12): 1536-1542
[24] Weed L L. Medical records that guide and teach. New England Journal of Medicine, 1968, 278(12): 593-600
[25] Jacobs L. Interview with Lawrence Weed, MD——the father of the problem-oriented medical record looks ahead. The Permanente Journal, 2009, 13(3): 84-89
[26] Bossen C. Evaluation of a computerized problem-oriented medical record in a hospital department: does it support daily clinical practice? International Journal of Medical Informatics, 2007, 76(8): 592-600
[27] Tilstra S. In Search of the Holy Grail: How to Ensure the Perfect Progress Note [Online], available: org/Meetings/Past/2012/2012APDIMSpringConference/Pr-esentations/Documents/Spring%20Meeting/Wksp%20202_ Tilstra.pdf, December 27, 2013
[28] Ministry of Health of the People's Republic of China. The basic specifications of medical records writing (trial) [Online], available: , December 27, 2013(中華人民共和國衛(wèi)生部. 病歷書寫基本規(guī)范 [Online], available: 15.htm, December 27, 2013)
[29] Lynette H, Sager N. Automatic information formatting of a medical sublanguage. In: Proceedings of the 1982 Sublanguage: Studies of Language in Restricted Semantic Domains. Berlin, German: Walter de Gruyter, 1982. 27-80
[30] Friedman C, Kra P, Rzhetsky A. Two biomedical sublanguages: a description based on the theories of Zellig Harris. Journal of Biomedical Informatics, 2002, 35(4): 222-235
[31] Meystre S M, Savova G K, Kipper-Schuler K C, Hurdle J F. Extracting information from textual documents in the electronic health record: a review of recent research. Yearbook of Medical Informatics, 2008, 47(Suppl 1): 128-144
[32] O'Donnell H C, Kaushal R, Barron Y, Callahan M A, Adelman R D, Siegler E L. Physicians' attitudes towards copy and pasting in electronic note writing. Journal of General Internal Medicine, 2009, 24(1): 63-68
[33] Hammond K W, Helbig S T, Benson C C, Brathwaite-Sketoe B M. Are electronic medical records trustworthy? Observations on copying, pasting and duplication. In: Proceedings of the 2003 American Medical Informatics Association 2003 Annual Symposium. Washington DC, USA: AMIA, 2003. 269-273
[34] Wilcox L, Lu J, Lai J, Feiner S, Jordan D. ActiveNotes: computer-assisted creation of patient progress notes. In: Proceedings of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems. New York, USA: ACM Press, 2009. 3323-3328
[35] Wilcox L, Lu J, Lai J, Feiner S, Jordan D. Physician-driven management of patient progress notes in an intensive care unit. In: Proceedings of the 28th International Conference Extended Abstracts on Human Factors in Computing Systems. New York, USA: ACM Press, 2010. 1879-1888
[36] Grishman R, Sundheim B. Message Understanding Conference-6: a brief history. In: Proceedings of the 16th conference on Computational linguistics-Volume 1. Stroudsburg, PA, USA: Association for Computational Linguistics, 1996. 466-471
[37] Lang Jun, Qin Bing, Liu Ting, Li Zheng-Hua, Li Sheng. Number type recognition of Chinese personal noun phrase. Acta Automatica Sinica, 2008, 34(8): 972-979 (郎君, 秦兵, 劉挺, 李正華, 李生. 中文人稱名詞短語單復(fù)數(shù)自動識別. 自動化學(xué)報, 2008, 34(8): 972-979)
[38] Tang Bu-Zhou, Wang Xiao-Long, Wang Xuan. Confidence-weighted online sequence labeling algorithm. Acta Automatica Sinica, 2011, 37(2): 188-195(湯步洲, 王曉龍, 王軒. 置信度加權(quán)在線序列標(biāo)注算法. 自動化學(xué)報, 2011, 37(2): 188-195)
[39] Doddington G, Mitchell A, Przybocki M, Ramshaw L, Strassel S, Weischedel R. The automatic content extraction (ACE) program tasks, data, and evaluation. In: Proceedings of the 2004 International Conference on Language Resources and Evaluation. Lisbon, Portugal: European Language Resources Association, 2004. 837-840
[40] Wang Ning, Ge Rui-Fang, Yuan Chun-Fa, Wong K F, Li Wen-Jie. Company name identification in Chinese financial domain. Journal of Chinese Information Processing, 2002, 16(2): 1-6 (王寧, 葛瑞芳, 苑春法, 黃錦輝, 李文捷. 中文金融新聞中公司名的識別. 中文信息學(xué)報, 2002, 16(2): 1-6)
[41] Lin X D, Peng H, Liu B. Chinese named entity recognition using support vector machines. In: Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. Guangzhou, China: IEEE, 2006. 4216-4220
[42] Zhao Jian. Research on Conditional Probabilistic Model and Its Application in Chinese Named Entity Recognition [Ph.D. dissertation], Harbin Institute of Technology, China, 2006(趙健. 條件概率模型研究及其在中文名實體識別中的應(yīng)用 [博士學(xué)位論文], 哈爾濱工業(yè)大學(xué), 中國, 2006)
[43] Finkel J R, Grenager T, Manning C. Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2005. 363-370
[44] Finkel J R, Manning C. Joint parsing and named entity recognition. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2009. 326-334
[45] Nadeau D, Sekine S. A survey of named entity recognition and classification. Lingvisticae Investigationes, 2007, 30(1): 3-26
[46] Ke X, Li S Z. Chinese organization name recognition based on co-training algorithm. In: Proceedings of the 3rd International Conference on Intelligent System and Knowledge Engineering. Xiamen, China: IEEE, 2008. 771-777
[47] Nadeau D. Semi-supervised Named Entity Recognition: Learning to Recognize 100 Entity Types with Little Supervision [Ph.D. dissertation], University of Ottawa, Canada, 2007
[48] Ando R K, Zhang T. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 2005, 6: 1817-1853
[49] Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 2011, 12: 2493-2537
[50] Zhang Qi. Research on Entity Relation Recognition in Information Extraction [Ph.D. dissertation], University of Science and Technology of China, China, 2010 (張奇. 信息抽取中實體關(guān)系識別研究 [博士學(xué)位論文], 中國科學(xué)技術(shù)大學(xué), 中國, 2010)
[51] Swanson D R. Complementary structures in disjoint science literatures. In: Proceedings of the 14th annual international ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1991. 280-289
[52] Cohen A M, Hersh W R. A survey of current work in biomedical text mining. Briefings in Bioinformatics, 2005, 6(1): 57-71
[53] Chen J X. Automatic Relation Extraction Among Named Entities from Text Contents [Ph.D. dissertation], National University of Singapore, Singapore, 2006
[54] Che Wan-Xiang, Liu Ting, Li Sheng. Automatic entity relation extraction. Journal of Chinese Information Processing, 2004, 19(2): 1-6(車萬翔, 劉挺, 李生. 實體關(guān)系自動抽取. 中文信息學(xué)報, 2004, 19(2): 1-6)
[55] Chinchor N. MUC-7 named entity task definition (Version 3.5). In: Proceedings of the 7th Message Understanding Conference. Fairfax, Virginia, USA, 1998. Appendix E [Online], available: M/M98/M98-1028.pdf
[56] Aone C, Ramos-Santacruz M. REES: a large-scale relation and event extraction system. In: Proceedings of the 6th Conference on Applied Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2000. 76-83
[57] Agichtein E, Gravano L. Snowball: Extracting relations from large plain-text collections. In: Proceedings of the 5th ACM conference on Digital libraries. New York, USA: ACM, 2000. 85-94
[58] Bunescu R C, Mooney R J. Learning to extract relations from the web using minimal supervision. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL' 07). Prague, Czech Republic, 2007. 576-583
[59] Zhang Z. Weakly-supervised relation classification for information extraction. In: Proceedings of the 13th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2004. 581-588
[60] Hasegawa T, Sekine S, Grishman R. Discovering relations among named entities from large corpora. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2004. 415
[61] Chen J X, Ji D D, Tan C L, Niu Z Y. Unsupervised feature selection for relation extraction. In: Proceedings of the 2005 International Joint Conference on Natural Language Processing. Jeju Island, Korea: Springer, 2005. 262-267
[62] Zhang Zhi-Tian. The Research of Relation Extraction with Unsupervised Method [Master dissertation], Harbin Institute Technology, China, 2007(張志田. 無監(jiān)督關(guān)系抽取方法研究 [碩士學(xué)位論文], 哈爾濱工業(yè)大學(xué), 中國, 2007)
[63] Zhang Y, Zhou J. A trainable method for extracting Chinese entity names and their relations. In: Proceedings of the 2nd Workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2000. 66-72
[64] Suchanek F M, Ifrim G, Weikum G. Combining linguistic and statistical analysis to extract relations from web documents. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2006. 712-717
[65] Sleator D, Temperley D. Parsing English with a Link Grammar, Technical Report CMU-CS-91-196, School of Computer Science, Carnegie Mellon University, USA, 1991
[66] Brin S. Extracting patterns and relations from the world wide web. The World Wide Web and Databases, 1999, 1590(2): 172-183
[67] Ning Hai-Yan. Comparative Study of Automatic Entity Relation Extraction [Master dissertation], Harbin Insititute of Technology, China, 2010 (寧海燕. 實體關(guān)系自動抽取技術(shù)的比較研究 [碩士學(xué)位論文], 哈爾濱工業(yè)大學(xué), 中國, 2010)
[68] Fader A, Soderland S, Etzioni O. Identifying relations for open information extraction. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011. 1535-1545
[69] Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka E R, Mitchell T M. Toward an architecture for never-ending language learning. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence. Georgia, USA: AAAI, 2010. 1306-1313
[70] Suchanek F M, Kasneci G, Weikum G. YAGO: A core of semantic knowledge unifying wordnet and Wikipedia. In: Proceedings of the 16th International Conference on World Wide Web. New York, USA: ACM, 2007. 697-706
[71] Biega J, Kuzey E, Suchanek F M. Inside YAGO2s: a transparent information extraction architecture. In: Proceedings of the 22nd International Conference on World Wide Web Companion. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2013. 325-328
[72] Kim J D, Ohta T, Tateisi Y, Tsujii J. GENIA corpus——a semantically annotated corpus for bio-textmining. Bioinformatics, 2003, 19(Suppl 1): 180-182
[73] Tanabe L, Xie N, Thom L H, Matten W, Wilbur W J. GENETAG: a tagged corpus for gene/protein named entity recognition. BMC Bioinformatics, 2005, 6(Suppl 1): S3
[74] Kim J D, Ohta T, Tsuruoka Y, Tateisi Y, Collier N. Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the 2004 International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications. Stroudsburg, PA, USA: Association for Computational Linguistics, 2004. 70-75
[75] Arighi C N, Roberts P M, Agarwal S, Bhattacharya S, Cesareni G, Chatr-Aryamontri A, Clematide S, Gaudet P, Giglio M G, Harrow I, Huala E, Krallinger M, Leser U, Li D, Liu F, Lu Z, Maltais L J, Okazaki N, Perfetto L, Rinaldi F, Saetre R, Salgado D, Srinivasan P, Thomas P E, Toldo L, Hirschman L, Wu C H. BioCreative III interactive task: an overview. BMC Bioinformatics, 2011, 12(Suppl 8): S4
[76] Xu Wei, Fu Bin, Liu Liu, Yuan Chun-Fa, Li Wen-Jie. Domain extension of Chinese named entity recognition. In: Proceedings of the 9th Chinese National Conference on Computatinal Linguistics. Dalian, China, 2007. 503-508 (徐薇, 付濱, 劉柳, 苑春法, 李文捷. 中文命名實體識別系統(tǒng)的領(lǐng)域擴(kuò)展, 第九屆全國計算語言學(xué)學(xué)術(shù)會議. 大連, 中國, 2007. 503-508)
[77] Uzuner O, Solti I, Xia F, Cadag E. Community annotation experiment for ground truth generation for the I2B2 medication challenge. Journal of the American Medical Informatics Association, 2010, 17(5): 519-523
[78] Baldridge J, Osborne M. Active learning and the total cost of annotation. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Barcelona, Spain: Association for Computational Linguistics, 2004. 9-16
[79] Settles B, Craven M, Friedland L. Active learning with real annotation costs. In: Proceedings of the 2008 NIPS Workshop on Cost-Sensitive Learning. Vancouver, Canada, 2008. 1-10
[80] Tomanek K, Wermter J, Hahn U. An approach to text corpus construction which cuts annotation costs and maintains reusability of annotated data. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, Czech Republic, 2007. 486-495
[81] Blum A, Mitchell T. Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory. New York, USA: ACM, 1998. 92-100
[82] Yarowsky D. Unsupervised word sense disambiguation rivaling supervised methods. In: Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 1995. 189-196
[83] Zhu X J, Goldberg A B. Introduction to semi-supervised learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2009, 3(1): 1-130
[84] Fernandes E R, Brefeld U. Learning from partially annotated sequences. In: Proceedings of the 2011 European Conference on Machine Learning and Knowledge Discovery in Databases (Volume Part I). Berlin, Heidelberg: Springer-Verlag, 2011. 407-422
[85] Lou X H, Hamprecht F. Structured learning from partial annotations. ArXiv Preprint, ArXiv Preprint, arXiv: 1206. 6421, 2012
[86] Hovy D, Hovy E. Exploiting partial annotations with EM training. In: Proceedings of the 2012 NAACL-HLT Workshop on the Induction of Linguistic Structure. Stroudsburg, PA, USA: Association for Computational Linguistics, 2012. 31-38
[87] Tsuboi Y, Kashima H, Oda H, Mori S, Matsumoto Y. Training conditional random fields using incomplete annotations. In: Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008). Manchster, UK: ACM, 2008. 897-904
[88] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
[89] Torrey L, Shavlik J. Transfer learning. Handbook of Research on Machine Learning Applications. Hershey, PA: IGI Global, 2009
[90] Bodenreider O. The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Research, 2004, 32(suppl 1): D267-D270
[91] Friedman C, Alderson P O, Austin J, Cimino J J, Johnson S B. A general natural-language text processor for clinical radiology. Journal of the American Medical Informatics Association, 1994, 1(2): 161-174
[92] Coden A, Savova G, Sominsky I, Tanenblatt M, Masanz J, Schuler K, Cooper J, Guan W, de Groen P C. Automatically extracting cancer disease characteristics from pathology reports into a disease knowledge representation model. Journal of biomedical informatics, 2009, 42(5): 937-949
[93] Savova G K, Masanz J, Ogren P V, Tanenblatt M, Masanz J, Schuler K, Cooper J, Guan W, de Groen Piet C. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Information Association, 2010, 17(5): 507-13
[94] Ferrucci D, Lally A. UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering, 2004, 10(3-4): 327-348
[95] Ye Feng, Chen Ying-Ying, Zhou Gen-Gui, Li Hao-Min, Li Ying. Intelligent recognition of named entity in electronic medical records. Chinese Journal of Biomedical Engineering, 2011, 30(2): 256-262 (葉楓, 陳鶯鶯, 周根貴, 李昊旻, 李瑩. 電子病歷中命名實體的智能識別. 中國生物醫(yī)學(xué)工程學(xué)報, 2011, 30(2): 256-262)
[96] Li D C, Kipper-Schuler K, Savova G. Conditional random fields and support vector machines for disorder named entityrecognition in clinical texts. In: Proceedings of the 2008 Workshop on Current Trends in Biomedical Natural Language Processing. Morristown, NJ, USA: Association for Computational Linguistics, 2008. 94-95
[97] Jiang M, Chen Y, Liu M, Rosenbloom S T, Mani S, Denny J C, Xu H. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal of the American Medical Informatics Association, 2011, 18(5): 601-606
[98] Jonnalagadda S, Cohen S T, Wu S, Gonzalez G. Enhancing clinical concept extraction with distributional semantics. Journal of Biomedical Informatics, 2012, 45(1): 129-140
[99] de Bruijn B, Cherry C, Kiritchenko S, Martin J, Zhu X. Machine-learned solutions for three stages of clinical information extraction: the state of the art at I2B2 2010. Journal of the American Medical Informatics Association, 2011, 18(5): 557-562
[100] Ogren P, Savova G, Chute C. Constructing evaluation corpora for automated clinical named entity recognition. In: Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC'08). Marrakech, Morocco: European Language Resources Association, 2008. 28-30
[101] Uzuner O, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. Journal of the American Medical Informatics Association, 2007, 15(1): 14-24
[102] Uzuner O. Recognizing obesity and comorbidities in sparse data. Journal of the American Medical Informatics Association, 2009, 16(4): 561-570
[103] Aronow D B, Fangfang F, Croft W B. Ad hoc classification of radiology reports. Journal of the American Medical Informatics Association, 1999, 6(5): 393-411
[104] Goryachev S, Sordo M, Zeng Q T, Ngo L. Implementation and Evaluation of Four Different Methods of Negation Detection, Technical Report, Decision Systems Group, Harvard Medical School, 2006
[105] Mutalik P G, Deshpande A, Nadkarni P M. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. Journal of the American Medical Informatics Association, 2001, 8(6): 598-609
[106] Sohn S, Wu S, Chute C G. Dependency parser-based negation detection in clinical narratives. In: Proceedings of the 2012 AMIA Summits on Translational Science. San Francisco, USA: AMIA, 2012. 1-8
[107] Harkema H, Dowling J N, Thornblade T, Chapman W W. ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of Biomedical Informatics, 2009, 42(5): 839-851
[108] Uzuner O, Zhang X, Sibanda T. Machine learning and rule-based approaches to assertion classification. Journal of the American Medical Informatics Association, 2009, 16(1): 109-115
[109] Demner-Fushman D, Apostolova E, Islamaj D R, Lang F M, Neveol A, Shooshan S E, Aronson A R. NLM's system description for the fourth I2B2/VA challenge. In: Proceedings of the 2010 I2B2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: I2B2, 2010
[110] Grouin C, Abacha A B, Bernhard D. CARAMBA: concept, assertion, and relation annotation using machine-learning based approaches. In: Proceedings of the 2010 I2B2/VA Workshop on Challenges in Natural Language Processing for Clinical Data. Boston, MA, USA: I2B2, 2010
[111] Clark C, Aberdeen J, Coarr M, Tresner-Kirsch D, Wellner B, Yeh A, Hirschman L. MITRE system for clinical assertion status classification. Journal of the American Medical Informatics Association, 18(5): 563-567
[112] Frunza O, Inkpen D. Extraction of disease-treatment semantic relations from biomedical sentences. In: Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. 91-98
[113] Rink B, Harabagiu S, Roberts K. Automatic extraction of relations between medical concepts in clinical texts. Journal of the American Medical Informatics Association, 2011, 18(5): 594-600
[114] Stone P J, Dunphy D C, Smith M S, Ogilvie D M. The General Inquirer: A Computer Approach to Content Analysis. Cambridge: MIT Press, 1966
[115] Ryan R J. Groundtruth Budgeting: A Novel Approach to Semi-Supervised Relation Extraction of Medical Language [Master dissertation], Massachusetts Institute of Technology, USA, 2011
[116] Wang X, Chused A, Elhadad N, Friedman C, Markatou M. Automated knowledge acquisition from clinical narrative reports. In: Proceedings of the 2008 AMIA Annual Symposium, 2008. 783-787
[117] Chen E S, Hripcsak G, Xu H, Markatou M, Friedman C. Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. Journal of the American Medical Informatics Association, 2008, 15(1): 87-98
[118] Roberts A, Gaizauskas R, Hepple M. Extracting clinical relationships from patient narratives. In: Proceedings of the 2008 Workshop on Current Trends in Biomedical Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2008. 10-18
[119] Bekhuis T. Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy. Biomedical Digital Libraries, 2006, 3(1): 2
[120] Cameron D, Bodenreider O, Yalamanchili H, Danh T, Vallabhaneni S, Thirunarayan K, Sheth A P, Rindflesch T C. A graph-based recovery and decomposition of Swanson's hypothesis using semantic predications. Journal of Biomedical Informatics, 2013, 46(2): 238-251
[121] Chapman W W, Nadkarni P M, Hirschman L, D'Avolio D W, Savova G K, Uzuner O. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. Journal of the American Medical Informatics Association, 2011, 18(5): 540-543
[122] Pestian J P, Brew C, Matykiewicz P, Hovermale D J, Johnson N, Cohen K B. A shared task involving multi-label classification of clinical free text. In: Proceedings of the 2007 Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2007. 97-104
[123] Pestian J P, Matykiewicz P, Linn-Gust M. What's in a note: construction of a suicide note corpus. Biomedical Informatics Insights, 2012, 5: 1-6
[124] Voorhees E, Tong R. Overview of the TREC 2012 medical records track. In: Proceedings of the 21st Text REtrieval Conference. Gaithersburg, MD: National Institute for Standards and Technology, 2008. trec21/papers/MED12OVERVIEW.pdf
[125] Jiang Zhi-Peng, Zhao Fang-Fang, Guan Yi, Yang Jin-Feng. Research on Chinese electronic medical record oriented lexical corpus annotation. High Technology Letters, 2014, 24(6): 609-615 (蔣志鵬, 趙芳芳, 關(guān)毅, 楊錦鋒. 面向中文電子病歷的詞法語料標(biāo)注研究. 高技術(shù)通訊, 2014, 24(6): 609-615)
[126] Xia F. The Segmentation Guidelines for the Penn Chinese Treebank (3.0). Technical Report IRCS-00-06, University of Pennsylvania, USA, 2000
[127] Xia F. The Part-of-Speech Tagging Guidelines for the Penn Chinese Treebank (3.0). Technical Report IRCS-00-06, University of Pennsylvania, USA, 2000
[128] Xue N, Xia F. The Bracketing Guide-lines for Penn Chinese Treebank Project. Technical Report IRCS-00-06, University of Pennsylvania, USA, 2000
[129] Chen Z, Perl Y, Halper M, Geller J, Gu H. Partitioning the UMLS semantic network. IEEE Transactions on Information Technology in Biomedicine, 2002, 6(2): 102-108
[130] Slaughter L, Ruland C, Rotegard A K. Mapping cancer patients' symptoms to UMLS concepts. In: Proceedings of the 2005 AMIA Annual Symposium, 2005. 699-703
[131] Jimeno-Yepes A J, Aronson A R. Knowledge-based biomedical word sense disambiguation: comparison of approaches. BMC Bioinformatics, 2010, 11(1): 569-580
[132] Jonquet C, Shah N H, Youn C H, Callendar C, Storey M A, Musen M A. NCBO annotator: semantic annotation of biomedical data. In: Proceedings of the 8th International Semantic Web Conference. Washington, DC, USA, 2009. 171-172
[133] Pedersen T, Pakhomov S, McInnes B, Liu Y. Measuring the similarity and relatedness of concepts in the medical domain. In: Proceedings of the 2nd ACM SIGHIT Symposium on International Health Informatics. New York, USA: ACM, 2012. 879-880
[134] Ruiz-Martinez J M, Valencia-Garcia R, Fernandez-Breis J T, Garcia-Sanchez T, Martinez-Bejar R. Ontology learning from biomedical natural language documents using UMLS. Expert Systems with Applications, 2011, 38(10): 12365-12378
[135] Rosse C, Mejino J. A reference ontology for biomedical informatics: the foundational model of anatomy. Journal of Biomedical Informatics, 2003, 36(6): 478-500
[136] Pisanelli D M, Battaglia M, De Lazzari C. ROME: a reference ontology in medicine. In: Proceedings of the 2007 Conference on New Trends in Software Methodologies, Tools and Techniques. Amsterdam, The Netherlands: IOS Press, 2007. 485-493
[137] Wang X, Thompson P, Tsujii J, Anani-adou S. Biomedical Chinese-English CLIR using an extended CMeSH resource to expand queries. In: Proceedings of the 8th International Conference on Language Resources and Evaluation. Istanbul, Turkey: European Language Resources Association, 2012. 1148-1155
[138] Shen Tong. The Chinesization and Formalization of Unified Medical Language System [Master dissertation], Harbin Insititute of Technology, China, 2013 (沈彤. 一體化醫(yī)學(xué)語言系統(tǒng)的中文化和形式化表示研究 [碩士學(xué)位論文], 哈爾濱工業(yè)大學(xué), 中國, 2013)

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