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Mining the Electronic Health Record for Disease Knowledge

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Book cover Biomedical Literature Mining

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1159))

Abstract

The growing amount and availability of electronic health record (EHR) data present enhanced opportunities for discovering new knowledge about diseases. In the past decade, there has been an increasing number of data and text mining studies focused on the identification of disease associations (e.g., disease–disease, disease–drug, and disease–gene) in structured and unstructured EHR data. This chapter presents a knowledge discovery framework for mining the EHR for disease knowledge and describes each step for data selection, preprocessing, transformation, data mining, and interpretation/validation. Topics including natural language processing, standards, and data privacy and security are also discussed in the context of this framework.

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Acknowledgment

The example clinical note in Fig. 2d was obtained with permission from MTSamples (http://www.mtsamples.com). This work was supported in part by the National Library of Medicine of the National Institutes of Health under award number R01LM011364. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors and Affiliations

  1. Center for Clinical and Translational Science, University of Vermont, Burlington, VT, USA

    Elizabeth S. Chen Ph.D. & Indra Neil Sarkar Ph.D., M.L.I.S.

  2. Division of General Internal Medicine, Department of Medicine, University of Vermont, Burlington, VT, USA

    Elizabeth S. Chen Ph.D.

  3. Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, USA

    Indra Neil Sarkar Ph.D., M.L.I.S.

  4. Department of Computer Science, University of Vermont, Burlington, VT, USA

    Elizabeth S. Chen Ph.D. & Indra Neil Sarkar Ph.D., M.L.I.S.

Authors
  1. Elizabeth S. Chen Ph.D.
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  2. Indra Neil Sarkar Ph.D., M.L.I.S.
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Correspondence to Elizabeth S. Chen Ph.D. .

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Editors and Affiliations

  1. GlaxoSmithKline, King of Prussia, Pennsylvania, USA

    Vinod D. Kumar

  2. GlaxoSmithKline, Hitchin, Hertfordshire, United Kingdom

    Hannah Jane Tipney

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Chen, E.S., Sarkar, I.N. (2014). Mining the Electronic Health Record for Disease Knowledge. In: Kumar, V., Tipney, H. (eds) Biomedical Literature Mining. Methods in Molecular Biology, vol 1159. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0709-0_15

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  • DOI: https://doi.org/10.1007/978-1-4939-0709-0_15

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