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Clinical Decision Support Systems pp 45–68Cite as

Data Mining and Clinical Decision Support Systems

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

Abstract

Data mining is a process of pattern and relationship discovery within large sets of data. Because of the large volume of data generated in healthcare settings, it is not surprising that healthcare organizations have been interested in data mining to enhance physician practices, disease management, and resource utilization. This chapter discusses a variety of data mining techniques that have been used to develop clinical decision support systems, including decision trees, neural networks, logistic regression, nearest neighbor classifiers. In addition, genetic algorithms, biologic and quantum computing, and big data analytics as well as methods of evaluating and comparing the different approaches are also discussed.

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Author information

Authors and Affiliations

  1. School of Health Professions, Department of Health Services Administration, University of Alabama at Birmingham, SHPB 590H, 1720 2nd Ave S, Birmingham, AL, 35294-1212, USA

    Bunyamin Ozaydin Ph.D.

  2. Academic Affairs, Samford University, 800 Lakeshore Drive, Birmingham, AL, 35229, USA

    J. Michael Hardin Ph.D.

  3. Pathology, Mount Sinai Health System, 1 Gustave L. Levy Place, New York, NY, 10029, USA

    David C. Chhieng M.D., M.B.A., M.S.H.I., M.S.E.M.

Authors
  1. Bunyamin Ozaydin Ph.D.
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  2. J. Michael Hardin Ph.D.
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  3. David C. Chhieng M.D., M.B.A., M.S.H.I., M.S.E.M.
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Correspondence to Bunyamin Ozaydin Ph.D. .

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Editors and Affiliations

  1. School of Health Professions, Department of Health Services Administration and Department of Medical Education, School of Medicine, University of Alabama at Birmingham , Birmingham, Alabama, USA

    Eta S. Berner

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Ozaydin, B., Hardin, J.M., Chhieng, D.C. (2016). Data Mining and Clinical Decision Support Systems. In: Berner, E. (eds) Clinical Decision Support Systems. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-31913-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-31913-1_3

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