Functional evaluation of out-of-the-box text-mining tools for data-mining tasks

Journal of the American Medical Informatics Association

Volumn 22, Issue 1, 2015, Pages 121-131

  Jung, Kenneth ^a   LePendu, Paea ^b   Iyer, Srinivasan ^b   Bauer Mehren, Anna ^b   Percha, Bethany ^a   Shah, Nigam H ^b  

^a STANFORD UNIVERSITY   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

Electronic health records; Natural language processing; Text mining

Indexed keywords

CILOSTAZOL;

ARTICLE; AUTOMATION; CLINICAL RESEARCH; DATA MINING; DRUG SAFETY; ELECTRONIC MEDICAL RECORD; HUMAN; LEARNING; LINGUISTICS; NATURAL LANGUAGE PROCESSING; OBESITY; VELOCITY; ADVERSE DRUG REACTION; ARTIFICIAL INTELLIGENCE; COMPARATIVE STUDY; DATA BASE; DRUG INTERACTION; ELECTRONIC HEALTH RECORD; EVALUATION STUDY; PROCEDURES;

ARTIFICIAL INTELLIGENCE; DATA MINING; DATABASES AS TOPIC; DRUG INTERACTIONS; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; ELECTRONIC HEALTH RECORDS; HUMANS; NATURAL LANGUAGE PROCESSING; OBESITY;

EID: 84930934865     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2014-002902     Document Type: Article

References (58)

1. Harpaz R, Haerian K, Chase HS, et al. Mining electronic health records for adverse drug effects using regression based methods. Proceedings of the 1st ACM International Health Informatics Symposium. Arlington, Virginia, USA. 1883008: ACM; 2010:100-7. http://dl.acm.org/citation. cfm?id=1883008
2. Haerian K, Varn D, Vaidya S, et al. Detection of pharmacovigilance- related adverse events using electronic health records and automated methods. Clin Pharmacol Ther 2012; 92:228-34.
3. Wang X, Hripcsak G, Markatou M, et al. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009;16:328-37.
4. Friedman C. Discovering novel adverse drug events using natural language processing and mining of the electronic health record. AMIE 2009: Proceedings of the 12th Conference on Artificial Intelligence in Medicine 2009:1-5.
5. Liu M, McPeek Hinz ER, Matheny ME, et al. Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. J Am Med Inform Assoc 2013;20:420-6.
6. Platt R, Carnahan RM, Brown JS, et al. The U.S. Food and Drug Administration's Mini-Sentinel program: status and direction. Pharmacoepidemiol Drug Saf 2012; 21(Suppl 1):1-8.
7. Iyer SV, Harpaz R, Lependu P, et al. Mining clinical text for signals of adverse drug-drug interactions. J Am Med Inform Assoc 2014;21:353-62.
8. Duke JD, Han X, Wang Z, et al. Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions. PLoS Comput Biol 2012;8:e1002614.
9. Lyalina S, Percha B, LePendu P, et al. Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records. J Am Med Inform Assoc 2013; 20(e2):e297-305.
10. Pathak J, Bailey KR, Beebe CE, et al. Normalization and standardization of electronic health records for highthroughput phenotyping: the SHARPn consortium. J Am Med Inform Assoc 2013;20(e2):e341-8.
11. Davis MF, Sriram S, Bush WS, et al. Automated extraction of clinical traits of multiple sclerosis in electronic medical records. J Am Med Inform Assoc 2013;20(e2):e334-40.
12. Jung K, LePendu P, Chen WS, et al. Automated detection of off-label drug use. PloS ONE 2014;9:e89324.
13. Chen ES, Hripcsak G, Xu H, et al. Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 2008;15: 87-98.
14. Zhu X, Cherry C, Kiritchenko S, et al. Detecting concept relations in clinical text: insights from a state-of-the-art model. J Biomed Inform 2013;46:275-85.
15. de Bruijn B, Cherry C, Kiritchenko S, et al. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J Am Med Inform Assoc 2011;18:557-62.
16. Patrick J, Li M. High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge. J Am Med Inform Assoc 2010; 17:524-7.
17. Poissant L, Taylor L, Huang A, et al. Assessing the accuracy of an inter-institutional automated patient-specific health problem list. BMC Med Inform Decis Mak 2010;10:10.
18. Birman-Deych E, Waterman AD, Yan Y, et al. Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care 2005;43:480-5.
19. Carroll RJ, Thompson WK, Eyler AE, et al. Portability of an algorithm to identify rheumatoid arthritis in electronic health records. J Am Med Inform Assoc 2012;19(e1):e162-9.
20. Xu H, Fu Z, Shah A, et al. Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. AMIA Annu Symp Proc 2011;2011: 1564-72.
21. Boland MR, Hripcsak G, Shen Y, et al. Defining a comprehensive verotype using electronic health records for personalized medicine. J Am Med Inform Assoc 2013; 20(e2):e232-8.
22. Pathak J, Kho AN, Denny JC. Electronic health recordsdriven phenotyping: challenges, recent advances, and perspectives. J Am Med Inform Assoc 2013;20(e2):e206-11.
23. Bauer-Mehren A, Lependu P, Iyer SV, et al. Network analysis of unstructured EHR data for clinical research. AMIA Jt Summits Transl Sci Proc 2013;2013:14-18.
24. Cole TS, Frankovich J, Iyer S, et al. Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis: a new model for EHR-based research. Pediatr Rheumatol Online J 2013; 11:45.
25. Chen L, Friedman C. Extracting phenotypic information from the literature via natural language processing. Stud Health Technol Inform 2004;107(Pt 2):758-62.
26. D'Avolio LW, Nguyen TM, Farwell WR, et al. Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC). J Am Med Inform Assoc 2010;17:375-82.
27. Savova GK, Masanz JJ, Ogren PV, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc 2010;17:507-13.
28. Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. J Am Med Inform Assoc 2011;18:544-51.
29. Deshmukh VG, Meystre SM, Mitchell JA. Evaluating the informatics for integrating biology and the bedside system for clinical research. BMC Med Res Methodol 2009;9:70.
30. Meystre SM, Savova GK, Kipper-Schuler KC, et al. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008:128-44. http://www.schattauer.de/en/ magazine/subject-areas/journals-a-z/imia-yearbook/imiayearbook- 2008/issue/840/manuscript/9830.html
31. Noy NF, Shah NH, Whetzel PL, et al. BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res 2009;37(Web Server issue):W170-3.
32. Lependu P, Iyer SV, Fairon C, et al. Annotation analysis for testing drug safety signals using unstructured clinical notes. J Biomed Semantics 2012;3(Suppl 1):S5.
33. Jonquet C, Shah NH, Musen MA. The Open Biomedical Annotator. Summit on Translat Bioinforma 2009;2009: 56-60.
34. Lowe HJ, Ferris TA, Hernandez PM, et al. STRIDE-An integrated standards-based translational research informatics platform. AMIA Annu Symp Proc 2009;2009:391-5.
35. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004;32(Database issue):D267-70.
36. Chapman WW, Bridewell W, Hanbury P, et al. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001;34: 301-10.
37. Shah NH, Bhatia N, Jonquet C, et al. Comparison of concept recognizers for building the Open Biomedical Annotator. BMC Bioinformatics 2009;10(Suppl 9):S14.
38. Unitex: http://www-igm.univ-mlv.fr/~unitex/
39. Friedman C. A broad-coverage natural language processing system. Proc AMIA Symp 2000:270-4. http:// www.ncbi.nlm.nih.gov/pmc/articles/PMC2243979/pdf/pro camiasymp00003-0305.pdf
40. Uzuner O. Recognizing obesity and comorbidities in sparse data. J Am Med Inform Assoc 2009;16:561-70.
41. Leeper NJ, Bauer-Mehren A, Iyer SV, et al. Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes. PloS ONE 2013;8:e63499.
42. Xu R, Musen MA, Shah NH. A comprehensive analysis of five million UMLS metathesaurus terms using eighteen million MEDLINE citations. AMIA Annu Symp Proc 2010;2010: 907-11.
43. Wu S, Liu H, Li D, et al. UMLS term occurrences in clinical notes: a large scale corpus analysis. J Am Med Inform Assoc 2012;19(e1):e149-56.
44. Bodenreider O, McCray AT. Exploring semantic groups through visual approaches. J Biomed Inform 2003;36: 414-32.
45. Nelson SJ, Zeng K, Kilbourne J, et al. Normalized names for clinical drugs: RxNorm at 6 years. J Am Med Inform Assoc Journal of the American 2011;18:441-8.
46. Health Fidelity: http://healthfidelity.com/health-fidelity-an nounces-research-program-support-advanced-use-unstruc tured-clinical-data
47. Lependu P, Iyer SV, Bauer-Mehren A, et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther 2013;93:547-55.
48. Knox C, Law V, Jewison T, et al. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res 2011;39(Database issue):D1035-41.
49. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837-45.
50. Uzuner O, South BR, Shen S, et al. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011;18:552-6.
51. Garla V, Lo Re VIII, Dorey-Stein Z, et al. The Yale cTAKES extensions for document classification: architecture and application. J Am Med Inform Assoc 2011;18:614-20.
52. Goldstein I, Uzuner O. Specializing for predicting obesity and its co-morbidities. J Biomed Inform 2009;42: 873-86.
53. Denny JC, Bastarache L, Ritchie MD, et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol 2013;31:1102-10.
54. Jensen PB, Jensen LJ, Brunak S. Mining electronic health records: towards better research applications and clinical care. Nat Rev Genet 2012;13:395-405.
55. Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA 2013;309:1351-2.
56. Murff HJ, FitzHenry F, Matheny ME, et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011;306:848-55.
57. Shah NH. Mining the ultimate phenome repository. Nat Biotechnol 2013;31:1095-7.
58. Halevy A, Norvig P, Pereira F. The Unreasonable Effectiveness of Data. IEEE Intell Syst 2009;24:8-12.