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Data Mining and Clinical Decision Support Systems

  • Bunyamin OzaydinEmail author
  • J. Michael Hardin
  • David C. Chhieng
Chapter
Part of the Health Informatics book series (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.

Keywords

Statistical pattern recognition Data mining Neural networks Decision trees Genetic algorithms Big data analytics Quantum computing 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bunyamin Ozaydin
    • 1
    Email author
  • J. Michael Hardin
    • 2
  • David C. Chhieng
    • 3
  1. 1.School of Health Professions, Department of Health Services AdministrationUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.Academic AffairsSamford UniversityBirminghamUSA
  3. 3.PathologyMount Sinai Health SystemNew YorkUSA

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