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Clinical Utility of Machine Learning and Longitudinal EHR Data

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Machine Learning in Healthcare Informatics

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 56))

Abstract

The widespread adoption of electronic health records in large health systems, combined with recent advances in data mining and machine learning methods, creates opportunities for the rapid acquisition and translation of knowledge for use in clinical practice. One area of great potential is in risk prediction of chronic progressive diseases from longitudinal medical records. In this Chapter, we illustrate this potential using a case study involving prediction of heart failure. Throughout, we discuss challenges and areas in need of further development.

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References

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Author information

Authors and Affiliations

  1. Sutter Health, Concord, CA, US

    Walter F. Stewart

  2. University of Pennsylvania, Philadelphia, PA, US

    Jason Roy

  3. IBM TJ Watson Research Center, Hawthorne, NY, US

    Jimeng Sun & Shahram Ebadollahi

Authors
  1. Walter F. Stewart
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  2. Jason Roy
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  3. Jimeng Sun
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  4. Shahram Ebadollahi
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Corresponding author

Correspondence to Walter F. Stewart .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Louisiana Tech University, Ruston, Louisiana, USA

    Sumeet Dua

  2. Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, Louisiana, USA

    Prerna Dua

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© 2014 Springer-Verlag Berlin Heidelberg

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Stewart, W.F., Roy, J., Sun, J., Ebadollahi, S. (2014). Clinical Utility of Machine Learning and Longitudinal EHR Data. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-40017-9_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40016-2

  • Online ISBN: 978-3-642-40017-9

  • eBook Packages: EngineeringEngineering (R0)

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