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Mining the Electronic Health Record for Disease Knowledge

Protocol
First Online:
Part of the Methods in Molecular Biology book series (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.

Key words

Electronic health record Knowledge discovery in databases Data mining Text mining Natural language processing Data warehouse Data privacy and security Standards 
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Notes

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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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Center for Clinical and Translational Science, University of VermontBurlingtonUSA
  2. 2.Division of General Internal Medicine, Department of MedicineUniversity of VermontBurlingtonUSA
  3. 3.Department of Microbiology and Molecular GeneticsUniversity of VermontBurlingtonUSA
  4. 4.Department of Computer ScienceUniversity of VermontBurlingtonUSA

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