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Rule Learning in Healthcare and Health Services Research

  • Janusz WojtusiakEmail author
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)

Abstract

Successful application of machine learning in healthcare requires accuracy, transparency, acceptability, ability to deal with complex data, ability to deal with background knowledge, efficiency, and exportability. Rule learning is known to satisfy the above criteria. This chapter introduces rule learning in healthcare, presents very expressive attributional rules, briefly describes the AQ21 rule learning system, and discusses three application areas in healthcare and health services research.

Keywords

Rule learning Attributional calculus AQ21 system Health services research Aggregated data Healthcare billing data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Machine Learning and Inference Laboratory, Department of Health Administration and PolicyGeorge Mason UniversityFairfaxUSA

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