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Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database

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Advances in Learning Classifier Systems (IWLCS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1996))

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Abstract

A stimulus-response LCS, called EpiCS, based upon the BOOLE and NEWBOOLE paradigms, was developed to work in single-step environments in which the goal is to generalize clinical decision rules from medical data by means of building explanatory and predictive models. This paper addresses the scalability of EpiCS to a large database, the Fatal Accident Reporting System (FARS), which is a large prospective database supported by the National Highway Traffic Safety Administration (NHTSA) of Transportation. This investigation used 1998 FARS data, the most recent complete year’s data available at this time. The performance of EpiCS in building explanatory and predictive models compared very favorably with a decision tree inducer and logistic regression applied to these tasks.

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Author information

Authors and Affiliations

  1. Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, 19104, Philadelphia, PA, USA

    John H. Holmes

Authors
  1. John H. Holmes
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Editor information

Editors and Affiliations

  1. Politecnico di Milano Dipartimento di Elettronica e Informazione, Artificial Intelligence and Robotics Laboratory, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Pier Luca Lanzi

  2. DaimlerChrysler AG Research and Technology,Cognition and Robotics, Alt-Moabit 96A, 10559, Berlin, Germany

    Wolfgang Stolzmann

  3. Prediction Dynamics, Concord, MA 01742, USA

    Stewart W. Wilson

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© 2001 Springer-Verlag Berlin Heidelberg

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Holmes, J.H. (2001). Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2000. Lecture Notes in Computer Science(), vol 1996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44640-0_8

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  • DOI: https://doi.org/10.1007/3-540-44640-0_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42437-6

  • Online ISBN: 978-3-540-44640-8

  • eBook Packages: Springer Book Archive

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