Skip to main content
SpringerLink
Log in

Natural Language Processing, Electronic Health Records, and Clinical Research

  • Chapter
  • First Online:
Book cover Clinical Research Informatics

Part of the book series: Health Informatics ((HI))

Abstract

Electronic health records (EHR) capture “real-world” disease and care processes and hence offer richer and more generalizable data for comparative ­effectiveness research than traditional randomized clinical trial studies. With the increasingly broadening adoption of EHR worldwide, there is a growing need to widen the use of EHR data to support clinical research. A big barrier to this goal is that much of the information in EHR is still narrative. This chapter describes the foundation of biomedical natural language processing and its common uses for extracting and transforming narrative information in EHR to support clinical research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sox HC, Greenfield S. Comparative effectiveness research: a report from the institute of medicine. Ann Intern Med. 2009;151:203–5.

    PubMed  Google Scholar 

  2. National Center for Research Resources (U.S.). Widening the use of electronic health record data for research [electronic resource]. Bethesda, MD. : National Institutes of Health, 2009.

    Google Scholar 

  3. National Institute of Child Health & Human Development. Clinical Research and Clinical Trials. http://www.nichd.nih.gov/health/clinicalresearch/. Accessed Aug 2011.

  4. Sung NS, Crowley WF, Genel M, Salber P, Sandy L, Sherwood LM, Johnson SB, Catanese V, Tilson H, Getz K, Larson EL, Scheinberg D, Reece EA, Slavkin H, Dobs A, Grebb J, Martinez RA, Korn A, Rimoin D. Central challenges facing the national clinical research enterprise. JAMA. 2003;289:1278–87.

    Article  PubMed  Google Scholar 

  5. Harris Interactive. Most physicians do not participate in clinical trials because of lack of opportunity, time, personnel support and resources. Rochester, NY, June 11, 2004. http://www.harrisinteractive.com/news/allnewsbydate.asp?NewsID=811. Accessed Aug 2011.

  6. National Center for Research Resources. Clinical and Translational Science Awards. http://www.ncrr.nih.gov/clinical%5Fresearch%5Fresources/clinical%5Fand%5Ftranslational%5Fscience%5Fawards/. Accessed Aug 2011.

  7. Garets D, Davis M. Electronic medical records vs. electronic health records: yes, there is a difference. A HIMSS Analytics White Paper, HIMSS Analytics, Chicago; 2005.

    Google Scholar 

  8. Garets D, Davis M. Electronic patient records, EMRs and EHRs: concepts as different as apples and oranges at least deserve separate names. Healthc Inform. 2005;22(10):53–4.

    PubMed  Google Scholar 

  9. Wikipedia. File: Electronic medical record.jpg. http://en.wikipedia.org/wiki/File:Electronic_medical_record.jpg. Accessed Dec 2011.

  10. Walker EP. More doctors are using electronic medical records 2010. http://www.medpagetoday.com/PracticeManagement/InformationTechnology/17862. Accessed Aug 2011.

  11. U.S. Census Bureau. National Totals: Vintage 2009. http://www.census.gov/popest/data/national/totals/2009/index.html. Accessed Dec 2011.

  12. Hazlehurst B, Mullooly J, Naleway A, Crane B. Detecting possible vaccination reactions in clinical notes. AMIA Annu Symp Proc. 2005; 2005:306–10.

    Google Scholar 

  13. Pakhomov S, Weston SA, Jacobsen SJ, Chute CG, Meverden R, Roger VL. Electronic medical records for clinical research: application to the identification of heart failure. Am J Manag Care. 2007;13:281–8.

    PubMed  Google Scholar 

  14. Liao KP, Cai T, Gainer V, Goryachev S, Zeng-treitler Q, Raychaudhuri S, Szolovits P, Churchill S, Murphy S, Kohane I, Karlson EW, Plenge RM. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken). 2010;62:1120–7.

    Article  Google Scholar 

  15. Brownstein JS, Murphy SN, Goldfine AB, Grant RW, Sordo M, Gainer V, Colecchi JA, Dubey A, Nathan DM, Glaser JP, Kohane IS. Rapid identification of myocardial infarction risk associated with diabetes medications using electronic medical records. Diabetes Care. 2010;33:526–31.

    Article  PubMed  Google Scholar 

  16. Reis BY, Kohane IS, Mandl KD. Longitudinal histories as predictors of future diagnoses of domestic abuse: modelling study. BMJ. 2009;339:b3677.

    Article  PubMed  Google Scholar 

  17. Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc. 2009;16:328–37.

    Article  PubMed  Google Scholar 

  18. Chute CG. The horizontal and vertical nature of patient phenotype retrieval: new directions for clinical text processing. Proc AMIA Symp. 2002:165–9.

    Google Scholar 

  19. Thadani SR, Weng C, Bigger JT, Ennever JF, Wajngurt D. Electronic screening improves efficiency in clinical trial recruitment. J Am Med Inform Assoc. 2009;16:869–73.

    Article  PubMed  Google Scholar 

  20. Embi PJ, Payne PRO. Clinical research informatics: challenges, opportunities and definition for an emerging domain. J Am Med Inform Assoc. 2009;16:316–27.

    Article  PubMed  Google Scholar 

  21. Kuehn BM. Institute of Medicine outlines priorities for comparative effectiveness research. JAMA. 2009;302:936–7.

    Article  PubMed  CAS  Google Scholar 

  22. Grishman R, Hirschman L, Nhan NT. Discovery procedures for sublanguage selectional patterns: initial experiments. Comput Linguist. 1986;12:205–15.

    Google Scholar 

  23. Harris Z. Mathematical structures of language. New York: Wiley; 1968.

    Google Scholar 

  24. Grishman R, Kittredge R. Analyzing language in restricted domains: sublanguage description and processing. New York: Routledge; 1986.

    Google Scholar 

  25. Johnson SB, Gottfried M. Sublanguage analysis as a basis for a controlled medical vocabulary. Proc Annu Symp Comput Appl Med Care. 1989:519–23.

    Google Scholar 

  26. Bronzino JD. The biomedical engineering handbook. New York: Springer; 2000.

    Google Scholar 

  27. Friedman C, Kra P, Rzhetsky A. Two biomedical sublanguages: a description based on the theories of Zellig Harris. J Biomed Inform. 2002;35:222–35.

    Article  PubMed  CAS  Google Scholar 

  28. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. New York: Springer; 2001.

    Google Scholar 

  29. Bishop C. Pattern recognition and machine learning (information science and statistics). New York: Springer; 2007.

    Google Scholar 

  30. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. San Francisco: Morgan Kaufmann Publishers Inc; 1988.

    Google Scholar 

  31. Michalski RS, Carbonell JG, Mitchell TM. Machine learning: an artificial intelligence approach. Los Altos: Morgan Kaufmann Pub; 1986.

    Google Scholar 

  32. Manning CD, Schütze H. Foundations of statistical natural language processing. Cambridge: MIT Press; 2000.

    Google Scholar 

  33. Bayes M, Price M. An essay towards solving a problem in the Doctrine of chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S. Philos Transact (1683–1775). 1763;53:370–418.

    Article  Google Scholar 

  34. Pearl J. Bayesian networks: a model of self-activated memory for evidential reasoning. In: Proceedings of the 7th cConference of the Cognitive Science Society, University of California, Irvine; 1985, p. 334, 329.

    Google Scholar 

  35. Verduijn M, Peek N, Rosseel PMJ, de Jonge E, de Mol BAJM. Prognostic Bayesian networks: I: rationale, learning procedure, and clinical use. J Biomed Inform. 2007;40:609–18.

    Article  PubMed  Google Scholar 

  36. Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. J Comput Biol. 2000;7:601–20.

    Article  PubMed  CAS  Google Scholar 

  37. Baum LE, Petrie T. Statistical inference for probabilistic functions of finite state Markov chains. Ann Math Stat. 1966;37:1554–63.

    Article  Google Scholar 

  38. Lukashin AV, Borodovsky M. GeneMark.hmm: new solutions for gene finding. Nucleic Acids Res. 1998;26:1107–15.

    Article  PubMed  CAS  Google Scholar 

  39. Yu L, Smith TF. Positional statistical significance in sequence alignment. J Comput Biol. 1999;6:253–9.

    Article  PubMed  CAS  Google Scholar 

  40. Kindermann R. Markov random fields and their applications, Contemporary mathematics, vol. 1. Providence: American Mathematical Society; 1950.

    Google Scholar 

  41. Komodakis N, Besbes A, Glocker B, Paragios N. Biomedical image analysis using Markov random fields & efficient linear programing. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6628–31.

    PubMed  Google Scholar 

  42. Lee N, Laine AF, Smith RT. Bayesian transductive Markov random fields for interactive segmentation in retinal disorders. In: World congress on medical physics and biomedical engineering, Munich; 7–12 Sept 2009. p. 227–30.

    Google Scholar 

  43. Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.

    Google Scholar 

  44. Pavlopoulos S, Stasis A, Loukis E. A decision tree – based method for the differential diagnosis of aortic stenosis from mitral regurgitation using heart sounds. Biomed Eng Online. 2004;3:21.

    Article  PubMed  Google Scholar 

  45. Suresh A, Karthikraja V, Lulu S, Kangueane U, Kangueane P. A decision tree model for the prediction of homodimer folding mechanism. Bioinformation. 2009;4:197–205.

    PubMed  Google Scholar 

  46. Pearl R, Reed LJ. A further note on the mathematical theory of population growth. Proc Natl Acad Sci USA. 1922;8:365–8.

    Article  PubMed  CAS  Google Scholar 

  47. Bagley SC, White H, Golomb BA. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol. 2001;54:979–85.

    Article  PubMed  CAS  Google Scholar 

  48. Gareen IF, Gatsonis C. Primer on multiple regression models for diagnostic imaging research. Radiology. 2003;229:305–10.

    Article  PubMed  Google Scholar 

  49. Vapnik VN. The nature of statistical learning theory. New York: Springer; 1995.

    Google Scholar 

  50. Brown MPS, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS, Ares M, Haussler D. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA. 2000;97:262–7.

    Article  PubMed  CAS  Google Scholar 

  51. Polavarapu N, Navathe SB, Ramnarayanan R, ul Haque A, Sahay S, Liu Y. Investigation into biomedical literature classification using support vector machines. Proc IEEE Comput Syst Bioinform Conf. 2005:366–74.

    Google Scholar 

  52. Takeuchi K, Collier N. Bio-medical entity extraction using support vector machines. In: Proceedings of the ACL 2003 workshop on natural language processing in biomedicine – Volume 13, Association for Computational Linguistics, Sapporo; 2003. p. 57–64.

    Google Scholar 

  53. Pan C, Yan X, Zheng C. Hard Margin SVM for biomedical image segmentation. In: Wang J, Liao X-F, Yi Z, editors. Advances in neural networks – ISNN 2005. Heidelberg: Springer; 2005. p. 754–9.

    Chapter  Google Scholar 

  54. Fix E, Jr. Discriminatory analysis: nonparametric discrimination: consistency properties. Technical Report, No. Project 21-49-004, Report Number 4, 1951:261–279.

    Google Scholar 

  55. Pan F, Wang B, Hu X, Perrizo W. Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis. J Biomed Inform. 2004;37:240–8.

    Article  PubMed  CAS  Google Scholar 

  56. Shanmugasundaram V, Maggiora GM, Lajiness MS. Hit-directed nearest-neighbor searching. J Med Chem. 2005;48:240–8.

    Article  PubMed  CAS  Google Scholar 

  57. Qi Y, Klein-Seetharaman J, Bar-Joseph Z. Random forest similarity for protein-protein interaction prediction from multiple sources. Pac Symp Biocomput. 2005:531–42.

    Google Scholar 

  58. Barbini P, Cevenini G, Massai MR. Nearest-neighbor analysis of spatial point patterns: application to biomedical image interpretation. Comput Biomed Res. 1996;29:482–93.

    Article  PubMed  CAS  Google Scholar 

  59. McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biol. 1990;52:99–115.

    PubMed  CAS  Google Scholar 

  60. Xue Q. Reddy BRS: late potential recognition by artificial neural networks. IEEE Trans Biomed Eng. 1997;44:132–43.

    Article  PubMed  CAS  Google Scholar 

  61. Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001;7:673–9.

    Article  PubMed  CAS  Google Scholar 

  62. Jerez-Aragonés JM, Gómez-Ruiz JA, Ramos-Jiménez G, Muñoz-Pérez J, Alba-Conejo E. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med. 2003;27:45–63.

    Article  PubMed  Google Scholar 

  63. Burke HB, Goodman PH, Rosen DB, Henson DE, Weinstein JN Jr FEH, Marks JR, Winchester DP, Bostwick DG. Artificial neural networks improve the accuracy of cancer survival prediction. Cancer. 1997;79:857–62.

    Article  PubMed  CAS  Google Scholar 

  64. Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proc of International Conference on Machine Learning (ICML), San Francisco, CA, 2001:282–89.

    Google Scholar 

  65. McCallum A, Freitag D, Pereira FCN. Maximum entropy Markov models for information extraction and segmentation. In: Proceedings of the seventeenth international conference on machine learning. San Francisco: Morgan Kaufmann Publishers Inc.; 2000. p. 591–8.

    Google Scholar 

  66. Settles B. ABNER: an open source tool for automatically tagging genes, proteins and other entity names in text. Bioinformatics. 2005;21:3191–2.

    Article  PubMed  CAS  Google Scholar 

  67. Leaman R, Gonzalez G. Banner: an executable survey of advances in biomedical named entity recognition. Pac Symp Biocomput. 2008;13:652–63.

    Google Scholar 

  68. Bundschus M, Dejori M, Stetter M, Tresp V, Kriegel H-P. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics. 2008;9:207.

    Article  PubMed  Google Scholar 

  69. Sarafraz F, Eales J, Mohammadi R, Dickerson J, Robertson D, Nenadic G. Biomedical event detection using rules, conditional random fields and parse tree distances. In: Proceedings of the workshop on BioNLP: shared task, Association for Computational Linguistics, Colorado; 2009. p. 115–8.

    Google Scholar 

  70. Forgy E. Cluster analysis of multivariate data: efficiency vs. interpretability of classifications. Biometrics. 1965;21:768.

    Google Scholar 

  71. Jardine N, Sibson R. Mathematical taxonomy. New York: Wiley; 1971.

    Google Scholar 

  72. McLachlan GJ, Basford KE. Mixture models. Inference and applications to clustering. New York: Marcel Dekker; 1988.

    Google Scholar 

  73. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA. 1999;96:2907.

    Article  PubMed  CAS  Google Scholar 

  74. De Smet F, Mathys J, Marchal K, Thijs G, De Moor B, Moreau Y. Adaptive quality-based clustering of gene expression profiles. Bioinformatics. 2002;18:735.

    Article  PubMed  Google Scholar 

  75. Sheng Q, Moreau Y, De Moor B. Biclustering microarray data by Gibbs sampling. Bioinformatics. 2003;19 Suppl 2:ii196–205.

    Article  PubMed  Google Scholar 

  76. Schafer J, Strimmer K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics. 2005;21:754.

    Article  PubMed  Google Scholar 

  77. Sager N, Friedman C, Chi E. The analysis and processing of clinical narrative. Medinfo. 1986;1986:86.

    Google Scholar 

  78. Sager N, Friedman C, Lyman MS, others. Medical language processing: computer management of narrative data. Reading: Addison-Wesley; 1987.

    Google Scholar 

  79. Friedman C, Alderson PO, Austin JH, Cimino JJ, Johnson SB. A general natural-language text processor for clinical radiology. J Am Med Inform Assoc. 1994;1:161–74.

    Article  PubMed  CAS  Google Scholar 

  80. Gold S, Elhadad N, Zhu X, Cimino JJ, Hripcsak G. Extracting structured medication event information from discharge summaries. AMIA Annu Symp Proc. 2008;2008:237–41.

    Google Scholar 

  81. Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc. 2010;17:19–24.

    Article  PubMed  CAS  Google Scholar 

  82. Haug PJ, Koehler S, Lau LM, Wang P, Rocha R, Huff SM. Experience with a mixed semantic/syntactic parser. Proc Annu Symp Comput Appl Med Care. 1995;1995:284–8.

    Google Scholar 

  83. Fiszman M, Chapman WW, Aronsky D, Evans RS, Haug PJ. Automatic detection of acute bacterial pneumonia from chest X-ray reports. J Am Med Inform Assoc. 2000;7:593–604.

    Article  PubMed  CAS  Google Scholar 

  84. Agarwal S, Hong Yu. Biomedical negation scope detection with conditional random fields. J Am Med Inform Assoc. 2010;17(6):696–701.

    Article  PubMed  Google Scholar 

  85. Agarwal S, Yu H. Detecting hedge cues and their scope in biomedical literature with conditional random fields. J Biomed Inform. 2010;43(6):953–61. Epub 2010 Aug 13.

    Article  PubMed  Google Scholar 

  86. Vincze V, Szarvas G, Farkas R, Mora G, Csirik J. The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes. BMC Bioinformatics. 2008;9:S9.

    Article  PubMed  Google Scholar 

  87. Li Z, Liu F, Antieau L, Cao Y, Yu H. Lancet: a high precision medication event extraction system for clinical text. J Am Med Inform Assoc. 2010;17(5):563–7.

    Article  PubMed  Google Scholar 

  88. Rennie J. Boosting with decision stumps and binary features. Relation. 2003;10:1666.

    Google Scholar 

  89. Cao Y, Liu F, Simpson P, Antieau L, Bennett A, Cimino JJ, Ely J, Yu H. AskHERMES: an online question answering system for complex clinical questions. J Biomed Inform. 2011;44(2):277–88. Epub 2011 Jan 21.

    Article  PubMed  Google Scholar 

  90. Cao Y-gang, Cimino JJ, Ely J, Yu H. Automatically extracting information needs from complex clinical questions. J Biomed Inform. 2010;43(6):962–71. Epub 2010 Jul 27.

    Article  PubMed  Google Scholar 

  91. Liu F, Kruse AM, Tur G, Hakkani-Tür D. Towards spoken clinical question answering: evaluating automatic speech recognition systems for clinical spoken questions. J Am Med Inform Assoc. 2011;18(5):625–30.

    Article  PubMed  Google Scholar 

  92. Stolcke A, Anguera X, Boakye K, Çetin Ö, Janin A, Peskin B, Wooters C, Zheng J. Further progress in meeting recognition: the ICSI-SRI Spring 2005 speech-to-text evaluation system. Vol. 3869, LNCS, MLMI workshop 2005;78:463–75.

    Google Scholar 

  93. Gupta D, Saul M, Gilbertson J. Evaluation of a deidentification (De-Id) software engine to share pathology reports and clinical documents for research. Am J Clin Pathol. 2004;121:176–86.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electric Engineering and Computer Science, University of Wisconsin Milwaukee, 3200 N Cramer Street, Milwaukee, WI, 53211, USA

    Feifan Liu Ph.D. & Hong Yu Ph.D.

  2. Department of Biomedical Informatics, Columbia University, 622 W 168th Street, VC-5, New York, NY, 10332, USA

    Chunhua Weng Ph.D.

Authors
  1. Feifan Liu Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunhua Weng Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Yu Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Feifan Liu Ph.D. .

Editor information

Editors and Affiliations

  1. College of Medicine, University of South Florida, Bruce B. Downs Blvd. 12901, Tampa, 33612-4799, Florida, USA

    Rachel L. Richesson

  2. College of Medicine, University of South Florida, Bruce B. Downs Blvd. 12901, Tampa, 33612-4799, Florida, USA

    James E. Andrews

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag London Limited

About this chapter

Cite this chapter

Liu, F., Weng, C., Yu, H. (2012). Natural Language Processing, Electronic Health Records, and Clinical Research. In: Richesson, R., Andrews, J. (eds) Clinical Research Informatics. Health Informatics. Springer, London. https://doi.org/10.1007/978-1-84882-448-5_16

Download citation

  • DOI: https://doi.org/10.1007/978-1-84882-448-5_16

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-447-8

  • Online ISBN: 978-1-84882-448-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation