Development and evaluation of RapTAT: A machine learning system for concept mapping of phrases from medical narratives

Journal of Biomedical Informatics

Volumn 48, Issue , 2014, Pages 54-65

  Gobbel, Glenn T ^a,b   Reeves, Ruth ^a,b   Jayaramaraja, Shrimalini ^a,b   Giuse, Dario ^b   Speroff, Theodore ^a,b   Brown, Steven H ^a,b   Elkin, Peter L ^c   Matheny, Michael E ^a,b  

^a GERIATRIC RESEARCH EDUCATION AND CLINICAL CENTER   (United States)

^b VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY AT BUFFALO   (United States)

Author keywords

Bayesian prediction; Machine learning; Natural language processing; Systematized nomenclature of medicine

Indexed keywords

COMPUTATIONAL LINGUISTICS; LEARNING ALGORITHMS; MAPPING; NATURAL LANGUAGE PROCESSING SYSTEMS; TERMINOLOGY; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; INFORMATION RETRIEVAL; LEARNING SYSTEMS; MEDICINE; OBJECT ORIENTED PROGRAMMING;

BAYESIAN PREDICTIONS; BETTER PERFORMANCE; CONCEPT MAPPING; DISCHARGE SUMMARY; MEDICAL CONCEPTS; NATURAL LANGUAGE PROCESSING; REFERENCE STANDARD; SINGLE DISCHARGES; SPECIFIC LANGUAGES;

LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS;

ARTICLE; BAYESIAN LEARNING; BOOTSTRAPPING; CONCEPT MAPPING; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MEDICAL LITERATURE; MEDICAL RECORD; NATURAL LANGUAGE PROCESSING; PRIORITY JOURNAL; REFERENCE DATABASE; SYSTEMATIZED NOMENCLATURE OF MEDICINE; ACCURACY; AUTOMATION; COMPUTER PROGRAM; FEASIBILITY STUDY; MEDICAL DOCUMENTATION; MEDICAL INFORMATICS; MEDICAL TERMINOLOGY; NOMENCLATURE; PROCESS OPTIMIZATION; QUALITY CONTROL; RAPID TEXT ANNOTATION TOOL; ALGORITHM; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CONTROLLED VOCABULARY; ELECTRONIC MEDICAL RECORD; FACTUAL DATABASE; HUMAN; PUBLIC HOSPITAL; REPRODUCIBILITY; STATISTICAL MODEL; UNIFIED MEDICAL LANGUAGE SYSTEM; UNITED STATES;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; AUTOMATION; BAYES THEOREM; DATABASES, FACTUAL; ELECTRONIC HEALTH RECORDS; HOSPITALS, VETERANS; HUMANS; MODELS, STATISTICAL; NATURAL LANGUAGE PROCESSING; REPRODUCIBILITY OF RESULTS; SOFTWARE; SYSTEMATIZED NOMENCLATURE OF MEDICINE; TENNESSEE; TERMINOLOGY AS TOPIC; UNIFIED MEDICAL LANGUAGE SYSTEM; VOCABULARY, CONTROLLED;

EID: 84899493552     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2013.11.008     Document Type: Article

References (60)

1. Elkin P.L., Froehling D.A., Wahner-Roedler D.L., et al. Comparison of natural language processing biosurveillance methods for identifying influenza from encounter notes. Ann Intern Med 2012, 156:11-18.
2. Hsiao I, Brusilovsky P. Modeling peer review in example annotation. In: ICCE, the 16th international conference on computers in education. Taipei, Taiwan: ICCE; 2008.
3. Crystal MR, Kubala F, MacIntyre R. Studies in data annotation effectiveness. In: Proceedings of the DARPA broadcast news workshop. Herndon, Virginia; 1999.
4. Hripcsak G., Rothschild A.S. Agreement, the F-measure, and reliability in information retrieval. J Am Med Inform Assoc 2005, 12:296-298.
5. Hersh W, Hickam DH, Haynes RB, et al. Evaluation of SAPHIRE: an automated approach to indexing and retrieving medical literature. In: Proc annu symp comput appl med care; 1991.
6. Friedman C., Johnson S.B. Natural language and text processing in biomedicine. Biomedical informatics computer applications in health care and biomedicine 2006, 312-343. Springer, New York (NY). 3rd ed. E.H. Shortliffe, J.J. Cimino (Eds.).
7. Denny J.C., Choma N.N., Peterson J.F., et al. Natural language processing improves identification of colorectal cancer testing in the electronic medical record. Med Decis Making 2012, 32:188-193.
8. Meystre S., Haug P.J. Automation of a problem list using natural language processing. BMC Med Inform Decis Mak 2005, 5:30.
9. 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, 16:328-337.
10. Chapman W.W., Nadkarni P.M., Hirschman L., et al. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. J Am Med Inform Assoc 2011, 18:540-543.
11. Gerbier S., Yarovaya O., Gicquel Q., et al. Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance. BMC Med Inform Decis Mak 2011, 11:50.
12. Haug P.J., Ranum D.L., Frederick P.R. Computerized extraction of coded findings from free-text radiologic reports. Work in progress. Radiology 1990, 174:543-548.
13. Jain NL, Knirsch CA, Friedman C, et al. Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports. In: AMIA annu symp proc; 1996.
14. Ranum D.L. Knowledge-based understanding of radiology text. Comput Methods Programs Biomed 1989, 30:209-215.
15. Friedman C, Knirsch C, Shagina L, et al. Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries. In: AMIA annu symp proc; 1999.
16. Brown S.H., Speroff T., Fielstein E.M., et al. EQuality: electronic quality assessment from narrative clinical reports. Mayo Clin Proc 2006, 81:1472-1481.
17. Brown SH, Elkin PL, Rosenbloom ST, et al. eQuality for all: extending automated quality measurement of free text clinical narratives. In: AMIA annu symp proc; 2008.
18. Denny JC, Peterson JF, Choma NN, et al. Development of a natural language processing system to identify timing and status of colonoscopy testing in electronic medical records. In: AMIA annu symp proc; 2009.
19. Chapman B.E., Lee S., Kang H.P., et al. Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm. J Biomed Inform 2011, 44:728-737.
20. Matheny M.E., Fitzhenry F., Speroff T., et al. Detection of infectious symptoms from VA emergency department and primary care clinical documentation. Int J Med Inform 2012, 81:143-156.
21. Murff H.J., FitzHenry F., Matheny M.E., et al. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011, 306:848-855.
22. Matheny ME, Fitzhenry F, Speroff T, et al. Detection of blood culture bacterial contamination using natural language processing. In: AMIA annu symp proc; 2009.
23. Kang N., Singh B., Afzal Z., et al. Using rule-based natural language processing to improve disease normalization in biomedical text. J Am Med Inform Assoc 2013, 20:876-881.
24. Tsuruoka Y., McNaught J., Ananiadou S. Normalizing biomedical terms by minimizing ambiguity and variability. BMC Bioinformatics 2008, 9(Suppl 3):S2.
25. Naderi N., Kappler T., Baker C.J., et al. OrganismTagger: detection, normalization and grounding of organism entities in biomedical documents. Bioinformatics 2011, 27:2721-2729.
26. Nadkarni P.M., Ohno-Machado L., Chapman W.W. Natural language processing: an introduction. J Am Med Inform Assoc 2011, 18:544-551.
27. Pakhomov S.V., Hanson P.L., Bjornsen S.S., et al. Automatic classification of foot examination findings using clinical notes and machine learning. J Am Med Inform Assoc 2008, 15:198-202.
28. Ruch P., Gobeill J., Lovis C., et al. Automatic medical encoding with SNOMED categories. BMC Med Inform Decis Mak 2008, 8(Suppl 1):S6.
29. 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.
30. Zou Q, Chu WW, Morioka C, et al. IndexFinder: a method of extracting key concepts from clinical texts for indexing. In: AMIA annu symp proc; 2003.
31. Denny JC, Smithers JD, Spickard 3rd A, et al. A new tool to identify key biomedical concepts in text documents. In: AMIA annu symp proc; 2002.
32. Friedman C. A broad-coverage natural language processing system. In: AMIA annu symp proc; 2000.
33. Zeng Q.T., Goryachev S., Weiss S., et al. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak 2006, 6:30.
34. Xu H., Stenner S.P., Doan S., et al. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc 2010, 17:19-24.
35. Aronson AR. Effective mapping of biomedical text to the UMLS metathesaurs: the MetaMap program. In: AMIA annu symp proc; 2001.
36. Tuttle M.S., Olson N.E., Keck K.D., et al. Metaphrase: an aid to the clinical conceptualization and formalization of patient problems in healthcare enterprises. Methods Inf Med 1998, 37:373-383.
37. Elkin PL, Cimino JJ, Lowe HJ, et al. Mapping to MeSH: the art of trapping MeSH equivalence from within narrative text. In: Proc 12th SCAMC; 1988.
38. Zhou L, Plasek JM, Mahoney LM, et al. Using medical text extraction, reasoning and mapping system (MTERMS) to process medication information in outpatient clinical notes. In: AMIA annu symp proc; 2011.
39. Srinivasan S, Rindflesch TC, Hole WT, et al. Finding UMLS Metathesaurus concepts in MEDLINE. In: AMIA annu symp proc; 2002.
40. Zieman YL, Bleich HL. Conceptual mapping of user's queries to medical subject headings. In: Proceedings: a conference of the American Medical Informatics Association/AMIA Annual Fall Symposium AMIA Fall Symposium; 1997.
41. D'Avolio L.W., Nguyen T.M., Goryachev S., et al. Automated concept-level information extraction to reduce the need for custom software and rules development. J Am Med Inform Assoc 2011, 18:607-613.
42. Stanfill M.H., Williams M., Fenton S.H., et al. A systematic literature review of automated clinical coding and classification systems. J Am Med Inform Assoc 2010, 17:646-651.
43. UMLS Reference Manual. Bethesda, MD: National Library of Medicine; 2009.
44. Aronson A.R., Lang F.M. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc 2010, 17:229-236.
45. Pakhomov S.V., Buntrock J.D., Chute C.G. Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniques. J Am Med Inform Assoc 2006, 13:516-525.
46. Elkin P.L., Brown S.H., Husser C.S., et al. Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists. Mayo Clin Proc 2006, 81:741-748.
47. Elkin PL, Froehling D, Wahner-Roedler D, et al. NLP-based identification of pneumonia cases from free-text radiological reports. In: AMIA annu symp proc; 2008.
48. Uzuner O., South B.R., Shen S., et al. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011, 18:552-556.
49. Nigam K., McCallum A., Mitchell T. Semi-supervised text classification using EM. Semi-supervised learning 2006, MIT Press, Cambridge, Mass. O. Chapelle, B. Schölkopf, A. Zien (Eds.).
50. Alpaydin E. Introduction to machine learning 2004, MIT Press, Cambridge, Mass.
51. Manning C.D., Raghavan P., Schütze H. Introduction to information retrieval 2008, Cambridge University Press, New York.
52. Steyberg E.W., Harrell F.E., Borsboom G.J.J.M., et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol 2001, 54:774-781.
53. Forman G., Scholz M. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. ACM SIGKDD Explorations Newletter 2010, 12:49-57.
54. National Library of Medicine NIoH. Lexical tools; 2013. [accessed 2013]. http://lexsrv3.nlm.nih.gov/LexSysGroup/Projects/lvg/2013/docs/userDoc/tools/lvg.html.
55. Charniak E. Immediate-head parsing for language models. In: Proceedings of the 39th annual meeting of the association for, computational linguistics; 2001.
56. Gibbs J.P., Poston D.L. The division of labor: conceptualization and Related Measures. Soc Forces 1975, 53:468-476.
57. Mitchell T.M. Machine learning 1997, McGraw-Hill, Boston, MA.
58. 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, 18:557-562.
59. Chowdhury MFM, Lavelli A. Disease mention recognition with specific features. In: Proceedings of the 2010 workshop on biomedical natural language processing; 2010. p. 83-90.
60. Brown SH, Elkin PL, Bauer BA, et al. SNOMED CT: utility for a general medical evaluation template. In: AMIA annu symp proc; 2006.