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Natural Language Processing in Health Care and Biomedicine

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Biomedical Informatics

Abstract

After reading this chapter, you should know the answers to these questions:

  • Why is natural language processing important?

  • What are the potential uses for natural language processing (NLP) in the biomedical and health domains?

  • What forms of knowledge are used in NLP?

  • What are the principal techniques of NLP?

  • What are challenges for NLP in the clinical, biological, and health consumer domains?

This chapter is adapted from an earlier version in the third edition authored by Carol Friedman and Stephen B. Johnson.

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Notes

  1. 1.

    Unless stated otherwise, the general domain and the topics of text materials discussed in this chapter refer to biomedicine and health.

  2. 2.

    http://www.ncbi.nlm.nih.gov/pmc (Accessed 4/26/13).

  3. 3.

    http://www.dbmi.pitt.edu/nlpfront. (Accessed 4/26/13).

  4. 4.

    orbit.nlm.nih.gov (Accessed 4/19/13).

  5. 5.

    www.nltk.org (Accessed 4/18/13).

  6. 6.

    www.alias-i.com/lingpipe/ (Accessed 4/18/13).

  7. 7.

    http://incubator.apache.org/opennlp/ (Accessed 4/19/13).

  8. 8.

    http://uima.apache.org/index.html (Accessed 4/19/13).

References

  • Aronow, D.B., Cooley, J.R., & Soderland, S. (1995). Automated identification of episodes of asthma exacerbation for quality measurement in a computer-based medical record. Proceedings of the Annual Symposium on Computer Applications in Medical Care, 309–313.

    Google Scholar 

  • Aronow, D., Feng, F., & Croft, W. B. (1999). Ad hoc classification of radiology reports. Journal of the American Medical Informatics Association: JAMIA, 6(5), 343–411.

    Article  Google Scholar 

  • Aronson, A.R. (2001). Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. Proceedings of AMIA Symposium, 17–21.

    Google Scholar 

  • Baud, R., Rassinoux, A. M., & Sherrer, J. R. (1992). Natural language processing and semantical representation of medical texts. Methods of Information in Medicine, 31, 117–125.

    PubMed  CAS  Google Scholar 

  • Baud, R., Lovis, C., Rassinoux, A. M., Michel, P. A., & Scherrer, J. R. (1998). Automatic extraction of linguistic knowledge from an international classification. Studies in Health Technology and Informatics, 52(Pt 1), 581–585.

    PubMed  Google Scholar 

  • Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Wheeler, D.L. (2003) GenBank. Nucleic Acids Research, 31(1):23–27. Available at http://www.ncbi.nlm.nih.gov

    Google Scholar 

  • Bishop, C. (2007). Pattern recognition and machine learning. New York: Springer.

    Google Scholar 

  • Blake JA, Richardson JE, Bult CJ, Kadin JA, Eppig JT, & the members of the Mouse Genome Database Group. (2003). MGD: The mouse genome database. Nucleic Acids Research, 31, 193–195. Available at http://www.informatics.jax.org/

    Google Scholar 

  • Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., & Schneider, M. (2003). The SWISS-PROT protein knowledgebase and its supplement TrEMBL. Nucleic Acids Research. 31, 365–370. Available at http://us.expasy.org/sprot/

  • Cao, Y., Liu, F., Simpson, P., Antieau, L., Bennett, A., Cimino, J. J., Ely, J., & Yu, H. (2011). AskHERMES: An online question answering system for complex clinical questions. Journal of Biomedical Informatics, 44(2), 277–288.

    Article  PubMed  Google Scholar 

  • Caporaso, J. G., Deshpande, N., Fink, J. L., Bourne, P. E., Cohen, K. B., & Hunter, L. (2008). Intrinsic evaluation of text mining tools may not predict performance on realistic tasks. Proceedings of the Pacific Symposium Biocomputing, 13, 640–651.

    Google Scholar 

  • Chapman, W. C., Dowling, J. N., & Wagner, M. M. (2004). Fever detection from free-text clinical records for biosurveillance. Journal of Biomedical Informatics, 37(2), 120–127.

    Article  PubMed  Google Scholar 

  • Christensen, L., Haug, P., & Fiszman, P. (2002). MPLUS: a probabilistic medical language understanding system. Proceedings of the ACL BioNLP, 29–36.

    Google Scholar 

  • Chuang, J.H., Friedman, C., & Hripcsak, G. (2002). A comparison of the charlson comorbidities derived from medical language processing and administrative data. Proceedings of the AMIA Symposium, 160–164.

    Google Scholar 

  • Deleger, L., Merkel, M., & Zweigenbaum, P. (2009). Translating medical terminologies through word alignment in parallel text corpora. Journal of Biomedical Informatics, 42(4), 692–701.

    Article  PubMed  Google Scholar 

  • Demner-Fushman, D., & Lin, J. (2007). Answering clinical questions with knowledge-based and statistical techniques. Computational Linguistics, 33(1), 63–103.

    Article  Google Scholar 

  • Elhadad, N. (2006). Comprehending technical texts: predicting and defining unfamiliar terms. Proceedings AMIA Symposium, 239–243.

    Google Scholar 

  • Elhadad, N., Kan, M. Y., Klavans, J. L., & McKeown, K. R. (2005). Customization in a unified framework for summarizing medical literature. Artificial Intelligence in Medicine, 33(2), 179–198.

    Article  PubMed  CAS  Google Scholar 

  • Evans, D. A., Cimino, J. J., Hersh, J. J., Huff, S. M., & Bell, D. S. (1994). Toward a medical-concept representation language. The Canon Group. Journal of the American Medical Informatics Association: JAMIA, 1(3), 207–217.

    Article  PubMed  CAS  Google Scholar 

  • Eysenbach, G., & Till, J. E. (2001). Ethical issues in qualitative research on internet communities. BMJ, 323(7321), 1103–1105.

    Article  PubMed  CAS  Google Scholar 

  • Friedman, C., Alderson, P. O., Austin, J., Cimino, J. J., & Johnson, S. B. (1994). A general natural language text processor for clinical radiology. Journal of the American Medical Informatics Association: JAMIA, 1(2), 161–174.

    Article  PubMed  CAS  Google Scholar 

  • Friedman, C., Huff, S. M., Hersh, W. R., Pattison-Gordon, E., & Cimino, J. J. (1995). The canon group’s effort: Working toward a merged model. Journal of the American Medical Informatics Association: JAMIA, 2(1), 4–18.

    Article  PubMed  CAS  Google Scholar 

  • Friedman, C., Kra, P., Krauthammer, M., Yu, H., & Rzhetsky, A. (2001). GENIES: A natural-langauge processing system for the extraction of molecular pathways from journal articles. Bioinformatics, 17(suppl), S74–S82.

    Article  PubMed  Google Scholar 

  • Friedman, C., Shagina, L., Lussier, Y., & Hripcsak, G. (2004). Automated encoding of clinical documents based on natural language processing. Journal of the American Medical Informatics Association: JAMIA, 11(5), 392–402.

    Article  PubMed  Google Scholar 

  • Fukuda, K., Tamura, A., Tsunoda, T., & Takagi, T. (1998). Toward information extraction: identifying protein names from biological papers. Proceedings of the Pacific Symposium on Biocomputing, 707–718.

    Google Scholar 

  • Grishman, R., & Kittredge, R. (Eds.). (1986). Analyzing language in restricted domains: Sublanguage description and processing. Hillsdale: Erlbaum Associates.

    Google Scholar 

  • Grishman, R., Sager, N., Raze, C., & Bookchin, B. (1973). The linguistic string parser. Proceedings of the National Computer Conference, 42, 427–434.

    Google Scholar 

  • Grosz, B., Joshi, A., & Weinstein, S. (1995). Centering: A framework for modeling the local coherence of discourse. Computational Linguistics, 2(21), 203–225.

    Google Scholar 

  • Hahn, U., Romacker, M., & Schulz, S. (1999). Discourse structures in medical reports – watch out! the generation of referentially coherent and valid text knowledge bases in the MEDSYNDIKATE system. International Journal of Medical Informatics, 53(1), 1–28.

    Article  PubMed  CAS  Google Scholar 

  • Hahn, U., Romacker, M., & Schulz, S. (2002). MEDSYNDIKATE: A natural language system for the extraction of medical information from finding reports. International Journal of Medical Informatics, 67(1/3), 63–74.

    Article  PubMed  Google Scholar 

  • Harkema, H., Dowling, J. N., Thornblad, T., & Chapman, W. W. (2009). ConText: An algorithm for determining negation, experiencer, and temporal status from clinical reports. Journal of Biomedical Informatics, 42(5), 839–851.

    Article  PubMed  Google Scholar 

  • Harris, Z. (1991). A theory of language and information – a mathematical approach. New York: Oxford University Press.

    Google Scholar 

  • Harris, Z., Gottfried, M., Ryckman, T., Mattick, P., Daladier, A., Harris, T., & Harris, S. (1989). The form of information in science – analysis of an immunology sublanguage. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Haug, P. J., Ranum, D. L., & Frederick, P. R. (1990). Computerized extraction of coded findings from free-text radiology reports. Radiology, 174, 543–548.

    PubMed  CAS  Google Scholar 

  • Haug, P., Koehler, S., Lau, L.M., Wang, P., Rocha, R., & Huff, S. (1994). A natural language understanding system combining syntactic and semantic techniques. Proceedings of the Annual Symposium on Computer Applications in Medical Care, 247–251.

    Google Scholar 

  • Hirschman, L., Yeh, A., Blaschke, C., & Valencia, A. (2005). Overview of BioCreAtIvE: Critical assessement of information extraction for biology. BMC Bioinformatics, 6(Suppl 1), S1.

    Article  PubMed  Google Scholar 

  • Hobbs, J. R., Appelt, D. E., Bear, J., Israel, D., Kameyama, M., Stickel, M., et al. (1996). FASTUS: A cascaded finite-state transducer for extracting information from natural-language text. In Finite state devices for natural language processing. Cambridge, MA: MIT Press.

    Google Scholar 

  • Hripcsak, G., Friedman, C., Alderson, P. O., DuMouchel, W., Johnson, S. B., & Clayton, P. D. (1995). Unlocking data from narrative reports: A study of natural language processing. Annals of Internal Medicine, 122(9), 681–688.

    Article  PubMed  CAS  Google Scholar 

  • Humphreys, K., Demetriou, G., & Gaizauskas, R. (2000). Two applications of information extraction to biological science journal articles: enzyme interactions and protein structures. Proceedings of the Pacific Symposium on Biocomputing, 505–516.

    Google Scholar 

  • Issel-Tarver, L., Christie, K.R., Dolinski, K., Andrada, R., Balakrishnan, R., Ball, C.A., Binkley, G., Dong, S., Dwight, S.S., Fisk, D.G., Harris, M., Schroeder, M., Sethuraman, A., Tse, K., Weng, S., Botstein, D., & Cherry, J.M. (2001). Saccharomyces genome database. Methods Enzymol, 350, 329–346. Available at http://www.yeastgenome.org/

    Google Scholar 

  • Jenssen, T.K., & Vinterbo, S. (2000). A set-covering approach to specific search for literature about human genes. Proceedings of the AMIA Symposium, 384–388.

    Google Scholar 

  • Jordan, D. A., McKeown, K. R., Concepcion, K. J., Feiner, S. K., & Hatzivassiloglou, V. (2001). Generation and evaluation of intraoperative inferences for automated health care briefings on patient status after bypass surgery. Journal of the American Medical Informatics Association: JAMIA, 8(3), 267–280.

    Article  PubMed  CAS  Google Scholar 

  • Jurafsky, D., & Martin, J. H. (2009). Speech and language processing. An introduction to natural language processing, computational linguistics and speech recognition. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Keselman, A., Tse, T., Crowell, J., Browne, A., Ngo, L., & Zeng, Q. (2007). Assessing consumer health vocabulary familiarity: An exploratory study. Journal of Medical Internet Research, 9(1), e5.

    Article  PubMed  Google Scholar 

  • Kim, J. D., Ohta, T., Tateisi, Y., & Tsujii, J. (2003). GENIA corpus – semantically annotated corpus for bio-textmining. Bioinformatics, 19(suppl 1), i180–i182.

    Article  PubMed  Google Scholar 

  • Kittredge, R., & Lehrberger, J. (Eds.). (1982). Sublanguage – studies of language in restricted semantic domains. New York: De Gruyter.

    Google Scholar 

  • Lindberg, D. A. B., Humphreys, B. L., & McCray, A. T. (1993). The unified medical language system. Methods of Information in Medicine, 32, 281–291.

    PubMed  CAS  Google Scholar 

  • Manning, C., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.

    Google Scholar 

  • Marcus, M., Santorini, B., & Marcinkiewicz, M. (1993). Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19, 313–330.

    Google Scholar 

  • Maroto, M., Reshef, R., Munsterberg, A. E., Koester, S., Goulding, M., & Lassar, A. B. (1997). Ectopic Pax-3 activates MyoD and Myf-5 expression in embryonic mesoderm and neural tissue. Cell, 89, 139–148.

    Article  PubMed  CAS  Google Scholar 

  • Minsky, M. (1975). A framework for representing knowledge. In P. H. Wintson (Ed.), The psychology of computer vision. New York: McGraw-Hill.

    Google Scholar 

  • Mutalik, P. G., Deshpande, A., & Nadkarni, P. M. (2001). Use of general-purpose negation detection to augment concept indexing of medical documents: A quantitative study using the UMLS. Journal of the American Medical Informatics Association: JAMIA, 8(6), 598–609.

    Article  PubMed  CAS  Google Scholar 

  • Nadkarni, P., Chen, R., & Brandt, C. (2001). UMLS concept indexing for production databases: A feasibility study. Journal of the American Medical Informatics Association: JAMIA, 8(1), 80–91.

    Article  PubMed  CAS  Google Scholar 

  • Palmer, M., Gildea, D., & Kingsbury, P. (2005). The proposition bank: An annotated corpus of semantic roles. Computational Linguistics, 31(1), 71–105.

    Article  Google Scholar 

  • Park, J. C., Kim, H. S., & Kim, J. J. (2001). Bidirectional incremental parsing for automatic pathway identification with combinatory categorial grammar. Proceedings of the Pacific Symposium on Biomcomputing, 6, 396–407.

    Google Scholar 

  • Pestian, J.P., & Matykiewicz, P. (2008). Classification of suicide notes using natural language processing. Proceedings of the ACL BioNLP, 96–97.

    Google Scholar 

  • Pestian, J.P., Brew, C., Matykiewicz, P., Hovermale, D.J., Johnson, N., Cohen, K.B., & Duch, W. (2007). A shared task involving multi-label classification of clinical free text. Proceedings of the Workshop on BioNLP, 97–104

    Google Scholar 

  • Rindflesch, T.C., Tanabe, L., Weinstein, J.N., & Hunter, L. (2000). EDGAR: extraction of drugs, genes and relations from the biomedical literature. Proceedings of the Pacific Symposium Biocomputing, 517–528.

    Google Scholar 

  • Saeed, M., Lieu, C., Raber, G., & Mark, R. G. (2002). MIMIC II: A massive temporal ICU patient database to support research in intelligent patient monitoring. Computers in Cardiology, 29, 641–644.

    PubMed  CAS  Google Scholar 

  • Sager, N. (1972). Syntactic formatting of science information. Proceedings of the AFIPS (pp. 791–800). In Kittredge, R., &Lehrberger, J., (Eds.), Reprinted in Sublanguage: Studies of language in restricted semantic domains (pp. 9–26). Berlin (1982): Walter de Gruyter.

    Google Scholar 

  • Sager, N. (1978). Natural language information formatting: The automatic conversion of texts to a structured data base. In M. C. Yovits (Ed.), Advances in computers (Vol. 17, pp. 89–162). New York: Academic Press.

    Google Scholar 

  • Sager, N. (1981). Natural language information processing: A computer grammer of english and its applications. Reading: Addison-Wesley.

    Google Scholar 

  • Sager, N., Friedman, C., & Lyman, M. (1987). Medical language processing – computer management of narrative data. Reading: Addison-Wesley.

    Google Scholar 

  • Sekimizu, T., Park, H. S., & Tsujii, J. (1998). Identifying the interaction between genes and gene products based on frequently seen verbs in Medline abstracts. Genome Informatics Ser Workshop on Genome Informatics, 9, 62–71.

    CAS  Google Scholar 

  • Spyns, P. (1996). Natural language processing in medicine: An overview. Methods of Information in Medicine, 35, 285–301.

    PubMed  CAS  Google Scholar 

  • The FlyBase Consortium. (2003). The FlyBase database of the Drosophila genome projects and community literature. Nucleic Acids Research, 31, 172–175. Available at http://flybase.org/

    Google Scholar 

  • The Gene Ontology Consortium. (2003). Gene ontology: tool for the unification of biology. Nature Genetics, 25, 25–29. Available at http://www.geneontology.org/

    Google Scholar 

  • Uzuner, O. (2009). Recognizing obesity and comorbidities in sparse data. Journal of the American Medical Informatics Association: JAMIA, 16(4), 561–570.

    Article  PubMed  Google Scholar 

  • Uzuner, O., Goldstein, I., Luo, Y., & Kohane, I. (2008). Identifying patient smoking status from medical discharge records. Journal of the American Medical Informatics Association: JAMIA, 15(1), 14–24.

    Article  PubMed  Google Scholar 

  • Uzuner, O., Solti, I., & Cadag, E. (2010). Extracting medication information from clinical text. Journal of the American Medical Informatics Association: JAMIA, 17(5), 514–518.

    Article  PubMed  Google Scholar 

  • Uzuner, O., South, B. R., Shen, S., & Duvall, S. L. (2011). 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Association: JAMIA, 18(5), 552–556.

    Article  PubMed  Google Scholar 

  • Vincze, V., Szarvas, G., Farkas, R., Mora, G., & Csirik, J. (2008). The BioScope corpus: Biomedical texts annotated for uncertainty, negation, and their scopes. BMC Bioinformatics, 9(S11), S9.

    Article  PubMed  Google Scholar 

  • Wang, X., Hripcsak, G., Markatou, M., & Friedman, C. (2009a). Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: A feasibility study. Journal of the American Medical Informatics Association: JAMIA, 16(3), 328–337.

    Article  PubMed  Google Scholar 

  • Wang, Y., Xiao, J., Suzek, T. O., et al. (2009b). PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37, W623–W633.

    Article  PubMed  CAS  Google Scholar 

  • Weeber, M., Mork, J., & Aronson, A. (2001). Developing a test collection for biomedical word sense disambiguation. Proceedings of the AMIA Symposium, 746–750.

    Google Scholar 

  • Weizenbaum, J. (1966). A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36–45.

    Article  Google Scholar 

  • Winograd, T. (1972). Understanding natural language. Cognitive Psychology, 3(1), 1–191.

    Article  Google Scholar 

  • Woods, W. (1973). Progress in NLU – an application to lunar geology. Proceeding of AFIPS, 42, 441–450.

    Google Scholar 

  • Yakushiji, A., Tateisi, Y., Miyao, Y., & Tsujii, J. (2001). Event extraction from biomedical papers using a full parser. Proceedings of the Pacific Symposium Biocomputing, 6, 408–419.

    Google Scholar 

  • Zhang, H., Fiszman, M., Shin, D., Miller, C. M., Rosemblat, G., & Rindflesch, T. C. (2011). Degree centrality for semantic abstraction summarization of therapeutic studies. Journal of Biomedical Informatics, 44(5), 830–838.

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC Bldg 5, New York, 10032, NY, USA

    Carol Friedman PhD & Noémie Elhadad PhD

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  1. Carol Friedman PhD
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  2. Noémie Elhadad PhD
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Correspondence to Carol Friedman PhD .

Editor information

Editors and Affiliations

  1. Departments of Biomedical Informatics at Columbia University and Arizona State University, New York, New York, USA

    Edward H. Shortliffe

  2. Bethesda, Maryland, USA

    James J. Cimino

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Friedman, C., Elhadad, N. (2014). Natural Language Processing in Health Care and Biomedicine. In: Shortliffe, E., Cimino, J. (eds) Biomedical Informatics. Springer, London. https://doi.org/10.1007/978-1-4471-4474-8_8

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