Best Practice & Research Clinical Anaesthesiology
Volume 23, Issue 1 , Pages 127-143, March 2009

Machine learning techniques to examine large patient databases

  • Geert Meyfroidt (Deputy Head of Clinics)

      Affiliations

    • Department of Intensive Care Medicine, UZ Leuven - Campus Gasthuisberg, Catholic University of Leuven, Herestraat 49, 3000 Leuven, Belgium
    • Corresponding Author InformationCorresponding author. Tel.: +32 16 34 40 21; Fax: +32 16 34 40 15.
  • ,
  • Fabian Güiza (PhD student)

      Affiliations

    • Department of Computer Sciences, Faculty of Engineering, Catholic University of Leuven, Leuven, Belgium
  • ,
  • Jan Ramon (Postdoctoral Researcher)

      Affiliations

    • Department of Computer Sciences, Faculty of Engineering, Catholic University of Leuven, Leuven, Belgium
  • ,
  • Maurice Bruynooghe (Professor, Head of Research Group Declarative Languages and Artificial Intelligence)

      Affiliations

    • Department of Computer Sciences, Faculty of Engineering, Catholic University of Leuven, Leuven, Belgium

Computerization in healthcare in general, and in the operating room (OR) and intensive care unit (ICU) in particular, is on the rise. This leads to large patient databases, with specific properties. Machine learning techniques are able to examine and to extract knowledge from large databases in an automatic way. Although the number of potential applications for these techniques in medicine is large, few medical doctors are familiar with their methodology, advantages and pitfalls. A general overview of machine learning techniques, with a more detailed discussion of some of these algorithms, is presented in this review.

Keywords: intensive care unit, operating room, computerization, patient data management system, machine learning, data mining, decision tree learning, Bayesian networks, Support Vector Machines, Gaussian processes

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PII: S1521-6896(08)00083-9

doi:10.1016/j.bpa.2008.09.003

Best Practice & Research Clinical Anaesthesiology
Volume 23, Issue 1 , Pages 127-143, March 2009