Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1

Journal of Biomedical Informatics

Volumn 58, Issue , 2015, Pages S11-S19

  Stubbs, Amber ^a   Kotfila, Christopher ^b   Uzuner, Özlem ^b  

^a SIMMONS COLLEGE   (United States)

^b STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Machine learning; Medical records; Natural language processing; Shared task

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOMATION; COMPUTATIONAL LINGUISTICS; HEALTH INSURANCE; LEARNING ALGORITHMS; LEARNING SYSTEMS;

ATTENTION TO DETAILS; CONDITIONAL RANDOM FIELD; HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACTS; LONGITUDINAL RECORDS; MEDICAL RECORD; NATURAL LANGUAGE PROCESSING; PROTECTED HEALTH INFORMATIONS; SHARED TASK;

NATURAL LANGUAGE PROCESSING SYSTEMS;

ARTICLE; AUTOMATION; CONTROLLED STUDY; MACHINE LEARNING; MEASUREMENT PRECISION; MEDICAL INFORMATION; MEDICAL RECORD; NARRATIVE; NATURAL LANGUAGE PROCESSING; PRIORITY JOURNAL; AUTOMATED PATTERN RECOGNITION; COHORT ANALYSIS; COMPARATIVE STUDY; COMPUTER SECURITY; CONFIDENTIALITY; CONTROLLED VOCABULARY; DATA MINING; ELECTRONIC HEALTH RECORD; EVALUATION STUDY; LONGITUDINAL STUDY; ORGANIZATION AND MANAGEMENT; PROCEDURES; VERBAL COMMUNICATION;

COHORT STUDIES; COMPUTER SECURITY; CONFIDENTIALITY; DATA MINING; ELECTRONIC HEALTH RECORDS; LONGITUDINAL STUDIES; NARRATION; NATURAL LANGUAGE PROCESSING; PATTERN RECOGNITION, AUTOMATED; VOCABULARY, CONTROLLED;

EID: 84940054677     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2015.06.007     Document Type: Article

References (43)

1. A. Stubbs, C. Kotfila, H. Xu, Ö. Uzuner, Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2, J. Biomed. Inf. 58S (2015) S67-S77.
2. El Emam K., Buckeridge D., Tamblyn R., Neisa A., Jonker E., Verma A. The re-identification risk of Canadians from longitudinal demographics. BMC Med. Inf. Decision Making 2011, 11:46.
3. P. Golle, Revisiting the uniqueness of simple demographics in the US population, Workshop on Privacy in the Electronic Society, 2006.
4. L. Sweeney, Uniqueness of Simple Demographics in the U.S. Population, Carnegie Mellon University, School of Computer Science, Data Privacy Laboratory, Technical Report LIDAP-WP4, Pittsburgh, 2000.
5. Amber Stubbs, Özlem Uzuner, Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus, J. Biomed. Inf. 58S (2015) S20-S29.
6. A. Stubbs, Ö. Uzuner, C.Kotfila, I. Goldstein, P. Szolovitz, Challenges in synthesizing replacements for PHI in narrative EMRs, in: Aris Gkoulalas-Divanis, Grigorios Loukides (Eds.), Medical Data Privacy Handbook, Springer, 2015.
7. B. Sundheim, Overview of results of the MUC-6 evaluation, in: Proceedings of the Sixth Message Understanding Conference (MUC-6), 1995.
8. Nancy Chinchor, MUC-7 Named Entity Task Definition version 3.5, 1997. (last accessed 06.01.15). http://www.itl.nist.gov.
9. Nancy Chinchor (a), Named Entity Scores - English, in: Message Understanding Conference Proceedings, Updated January 12, 2001. (last accessed 06.01.15). http://www.itl.nist.gov.
10. Hirschman L., Yeh A., Blaschke C., Valencia A. Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinf. 2005, 6:s1.
11. Hersh William, Voorhees Ellen TREC genomics special issue overview. Inform. Retrieval 2009, 12(1):1-15.
12. Jin-Dong Kim, Tomoko Ohta, Sampo Pyysalo, Yoshinobu Kano, Jun'ichi Tsujii, Overview of BioNLP'09 shared task on event extraction, in: Proceedings of the Workshop on BioNLP: Shared Task, Association for Computational Linguistics, Boulder, Colorado, June 2009, pp. 1-9.
13. Pyysalo S., Ohta T., Rak R., Sullivan D., Mao C., Wang C., Sobral B., Tsujii J., Ananiadou S. Overview of the ID, EPI and REL tasks of BioNLP shared task 2011. BMC Bioinf. 2012, 13.
14. C. Nédellec, R. Bossy, J.-D. Kim, J.-J. Kim, T. Ohta, S. Pyysalo, P. Zweigenbaum, in: Overview of BioNLP shared task 2013 Proceedings of the BioNLP Shared Task 2013 Workshop, Association for Computational Linguistics, 2013, pp. 1-7.
15. BioASQ project, Data. (last accessed 07.01.15). http://www.bioasq.org.
16. Uzuner Ö., Luo Y., Szolovits P. Evaluating the state-of-the-art in automatic de-identification. J. Am. Med. Inform. Assoc. 2007, 14(5):550-563.
17. Meystre Stephane M., Jeffrey Friedlin F., South Brett R., Shen Shuying, Samore Matthew H. Automatic de-identification of textual documents in the electronic health record: a review of recent research. BMC Med. Res. Methodol. 2010, 10(1):70. 10.1186/1471-2288-10-70.
18. Aberdeen John, Bayer Samuel, Yeniterzi Reyyan, Wellner Ben, Clark Cheryl, Hanauer David, Malin Bradley, Hirschman Lynette The MITRE identification scrubber toolkit: design, training, and assessment. Int. J. Med. Informatics 2010, 79(12):849-859. 10.1016/j.ijmedinf.2010.09.007.
19. Ferrández Oscar, South Brett R., Shen Shuying, Friedlin F.Jeffrey, Samore Matthew H., Meystre Stéphane M. BoB, a best-of-breed automated text de-identification system for VHA clinical documents. J. Am. Med. Inf. Assoc. 2013, 20(1):77-83. 10.1136/amiajnl-2012-001020.
20. Deleger Louise, Lingren Todd, Ni Yizhao, Kaiser Meghan, Stoutenborough Laura, Marsolo Keith, Kouril Michal, Molnar Katalin, Solti Imre Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research. J. Biomed. Inf. 2014, 50:173-183. 10.1016/j.jbi.2014.01.014.
21. Benjamin Wellner, Sequence Models and Ranking Methods for Discourse Parsing, Ph.D. Dissertation, Brandeis University, Waltham, MA, 2009.
22. J. Lafferty, A. McCallum, F. Pereira, Conditional random fields: probabilistic models for segmenting and labeling sequence data, in: Proc. 18th International Conference on Machine Learning, 2001.
23. Apache UIMA, 2006. (accessed May 2015). http://uima.apache.org.
24. Savova G.K., Masanz J.J., Ogren P.V., et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 2010, 17:507-513.
25. B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the Fifth Annual Workshop on Computational Learning Theory - COLT '92, 1992, p. 144. doi:10.1145/130385.130401.
26. Deleger L., Molnar K., Savova G., Xia F., Lingren T., Li Q., et al. Large-scale evaluation of automated clinical note de-identification and its impact on information extraction. J. Am. Med. Inform. Assoc. 2012, 10.1136/amiajnl-2012-001012.
27. A.C. McCallum, MALLET: a Machine Learning for Language Toolkit; 2002.
28. Vishesh Kumar, Amber Stubbs, Stanley Shaw, Özlem Uzuner, Creation of a new longitudinal corpus of clinical narratives, J. Biomed. Inf. 58S (2015) S6-S10.
29. A. Stubbs, Ö. Uzuner, De-identification of medical records through annotation, in: Nancy Ide, James Pustejovsky (Eds.), Handbook of Linguistic Annotation, Springer, Anticipated Publication, 2015.
30. Nancy Chinchor, The statistical significance of the MUC-4 results, in: Proceedings of the 4th Conference on Message Understanding, 1992, pp. 30-50.
31. Noreen E.W. Computer-Intensive Methods for Testing Hypotheses: An Introduction 1989, Wiley, New York.
32. Bin He, Yi Guan, Jianyi Cheng, Keting Cen, Wenlan Hua, CRFs based de-identification of medical records, J. Biomed. Inf., 2014 (i2b2 NLP supplement) J. Biomed. Inform. 58S (2015) S39-S46.
33. Zengjian Liu, Yangxin Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Haodi Li, Jingfeng Wang, Qiwen Deng, Suisong Zhu, Automatic de-identification of electronic medical records using token-level and character-level conditional random fields, J. Biomed. Inf.: 2014 (i2b2 NLP supplement) J. Biomed. Inform. 58S (2015) S47-S52.
34. Xu H., Stenner S.P., Doan S., Johnson K.B., Waitman L.R., Denny J.C. MedEx: a medication information extraction system for clinical narratives. J. Am. Med. Inf. Assoc.: JAMIA 2010, 17(1):19-24. 10.1197/jamia.M3378.
35. Manabu Torii, Jung-wei Fan, Wei-li Yang, Theodore Lee, Matthew T. Wiley, Daniel Zisook, Yang Huang, De-identification and risk factor detection in medical records, Paper presented at the Seventh i2b2 Shared Task and Workshop: Challenges in Natural Language Processing for Clinical Data. November 14, 2014, Washington DC. J. Biomed. Inform. 58S (2015) S164-S170.
36. Cyril Grouin, Clinical records de-identification using CRF and rule-based approaches, Poster session, presented at the Seventh i2b2 Shared Task and Workshop: Challenges in Natural Language Processing for Clinical Data. November 14, 2014, Washington DC.
37. Azad Dehghan, Aleksandar Kovačević, George Karystianis, John A. Keane, Goran Nenadic, Combining knowledge- and data-driven methods for de-identification of clinical narratives, J. Biomed. Inf., 2014 (i2b2 NLP supplement) J. Biomed. Inform. 58S (2015) S53-S59.
38. Tao Chen, Hidden Markov model using Dirchilet process for de-identification, J. Biomed. Inf., 2014 (i2b2 NLP supplement) J. Biomed. Inform. 58S (2015) S60-S66.
39. Orbanz P., Teh Y.W. Bayesian nonparametric models, Encyclopedia of Machine Learning 2010, Springer.
40. Baum L.E., Petrie T. Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 1966, 37(6):1554-1563.
41. Blei David M., Ng Andrew Y., Jordan Michael I. Latent dirichlet allocation. J. Mach. Learning Res. 2003, 3:993-1022.
42. Hui Yang, Jonathan Garibaldi, Automatic detection of protected health information from clinic narratives, J. Biomed. Inf., 2014 (i2b2 NLP supplement) J. Biomed. Inform. 58S (2015) S30-S38.
43. Rocio Guillen, An approach to de-identifying electronic medical records, Poster session, presented at the Seventh i2b2 Shared Task and Workshop: Challenges in Natural Language Processing for Clinical Data. November 14, 2014, Washington DC.