Automatic de-identification of textual documents in the electronic health record: A review of recent research

BMC Medical Research Methodology

Volumn 10, Issue , 2010, Pages

  Meystre, Stephane M ^a   Friedlin, F Jeffrey ^c   South, Brett R ^a,b   Shen, Shuying ^a,b   Samore, Matthew H ^a,b  

^a UNIVERSITY OF UTAH SCHOOL OF MEDICINE   (United States)

^b IDEAS Center SLCVA Healthcare System   (United States)

^c REGENSTRIEF INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; COMPUTER PROGRAM; CONFIDENTIALITY; HEALTH INSURANCE; HUMAN; LEGAL ASPECT; MEDICAL RECORD; REVIEW; UNITED STATES;

ALGORITHMS; CONFIDENTIALITY; ELECTRONIC HEALTH RECORDS; HEALTH INSURANCE PORTABILITY AND ACCOUNTABILITY ACT; HUMANS; SOFTWARE; UNITED STATES;

EID: 77955080124     PISSN: None     EISSN: 14712288     Source Type: Journal    
DOI: 10.1186/1471-2288-10-70     Document Type: Review

References (35)

1. NLM, The Hippocratic Oath 2002 http://www.nlm.nih.gov/hmd/greek/greek- oath.html
2. GPO, U.S, 45 C.F.R. 46 Protection of Human Subjects 2008 http://www.access.gpo.gov/nara/cfr/waisidx-08/45cfr46-08.html
3. GPO, U.S, 45 C.F.R. 164 Security and Privacy 2008 http://www.access.gpo. gov/nara/cfr/waisidx-08/45cfr164-08.html
4. Assessing the difficulty and time cost of de-identification in clinical narratives. DA Dorr, et al. Methods Inf Med 2006 246 52 16685332
5. Concept-match medical data scrubbing. How pathology text can be used in research. JJ Berman, Arch Pathol Lab Med 2003 680 6 12741890
6. Extracting information from textual documents in the electronic health record: a review of recent research. SM Meystre, et al. Yearb Med Inform 2008 128 44 18660887
7. Algorithmic De-identification of VA Medical Exam Text for HIPAA Privacy Compliance: Preliminary Findings. EM Fielstein SH Brown T Speroff, Medinfo 2004 1590
8. A software tool for removing patient identifying information from clinical documents. FJ Friedlin CJ McDonald, J Am Med Inform Assoc 2008 15 5 601 10 10.1197/jamia.M2702 18579831
9. Automated de-identification of free-text medical records. I Neamatullah, et al. BMC Med Inform Decis Mak 2008 8 32 10.1186/1472-6947-8-32 18652655
10. K Atkinson, Spell checking oriented word lists, revision 6 2004 http://sourceforge.net/projects/wordlist/files/SCOWL/Rev%206/scowl-6.tar.gz/ download
11. NLM, Unified Medical Language System (UMLS) - Metathesaurus 2009 http://www.nlm.nih.gov/research/umls/knowledge-sources/metathesaurus/index.html
12. NLM, Medical Subject Headings 2009 http://www.nlm.nih.gov/mesh/meshhome. html
13. Evaluating the state-of-the-art in automatic de-identification. O Uzuner Y Luo P Szolovits, J Am Med Inform Assoc 2007 550 63 10.1197/jamia.M2444 17600094
14. Development and evaluation of an open source software tool for deidentification of pathology reports. BA Beckwith, et al. BMC Med Inform Decis Mak 2006 12 10.1186/1472-6947-6-12 16515714
15. Evaluation of a deidentification (De-Id) software engine to share pathology reports and clinical documents for research. D Gupta M Saul J Gilbertson, Am J Clin Pathol 2004 176 86 10.1309/E6K33GBPE5C27FYU 14983930
16. NCI, caGrid and Infrastructure Overview 2009 https://cabig.nci.nih.gov/ working-groups/DSIC-SLWG/sharable/cagrid-overview
17. A broad-coverage natural language processing system. C Friedman, Proc AMIA Symp 2000 270 4 11079887
18. Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes? FP Morrison, et al. J Am Med Inform Assoc 2009 16 1 37 9 10.1197/jamia.M2862 18952938
19. Medical document anonymization with a semantic lexicon. P Ruch, et al. Proc AMIA Symp 2000 729 33 11079980
20. Replacing personally-identifying information in medical records, the Scrub system. L Sweeney, Proc AMIA Annu Fall Symp 1996 333 7 8947683
21. A successful technique for removing names in pathology reports using an augmented search and replace method. SM Thomas, et al. Proc AMIA Symp 2002 777 81 12463930
22. International Ispell 1996 http://www.lasr.cs.ucla.edu/geoff/ispell.html
23. Automatic Deidentification by using Sentence Features and Label Consistency. E Aramaki, et al. i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, Washington, DC 2006
24. HIDE: An Integrated System for Health Information De-identification. J Gardner L Xiong, Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems 2008 254 9 full-text
25. Identifying Personal Health Information Using Support Vector Machines. Y Guo, et al. i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, Washington, DC 2006
26. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. H Cunningham, et al. 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02), Phildelphia, PN 2002
27. Applying a SVM Based Chunker and a Text Classifier to the Deid Challenge. K Hara, i2b2 Workshop on Challenges in Natural Language Processing for Clinical Data, Washington, DC 2006
28. State-of-the-art anonymization of medical records using an iterative machine learning framework. G Szarvas R Farkas R Busa-Fekete, J Am Med Inform Assoc 2007 574 80 10.1197/jamia.M2441 17823086
29. A multilingual named entity recognition system using boosting and c4.5 decision tree learning algorithms. G Szarvas R Farkas A Kocsor, 9th Int Conf Disc Sci (DS2006), LNAI 2006 267 278
30. Identification of patient name references within medical documents using semantic selectional restrictions. RK Taira AA Bui H Kangarloo, Proc AMIA Symp 2002 757 61 12463926
31. A de-identifier for medical discharge summaries. O Uzuner, et al. Artif Intell Med 2008 42 1 13 35 10.1016/j.artmed.2007.10.001 18053696
32. Rapidly retargetable approaches to de-identification in medical records. B Wellner, et al. J Am Med Inform Assoc 2007 564 73 10.1197/jamia.M2435 17600096
33. A successful technique for removing names in pathology reports using an augmented search and replace method. SM Thomas, et al. Proc AMIA Symp 2002 777 81 12463930
34. Identifying patient smoking status from medical discharge records. O Uzuner, et al. J Am Med Inform Assoc 2008 15 1 14 24 10.1197/jamia.M2408 17947624
35. Recognizing Obesity and Co-morbidities in Sparse Data. O Uzuner, J Am Med Inform Assoc 2009 16 4 561 70 10.1197/jamia.M3115 19390096