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Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases

  • John H. Holmes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1813)

Abstract

A stimulus-response learning classifier system (LCS), EpiCS, was developed from the BOOLE and NEWBOOLE models to address the needs of knowledge discovery in databases used in clinical research. Two specific needs were investigated: the derivation of accurate estimates of disease risk, and the ability to deal with rare clinical outcomes. EpiCS was shown to have excellent classification accuracy, compared to logistic regression, when using risk estimates as the primary means for classification. This was especially true in data with low disease prevalence. EpiCS was designed to accommodate differential negative reinforcement when false positive or false negative decisions were made by the system. This feature was investigated to determine its effect on learning rate and classification accuracy. Tested across a range of disease prevalences, the learning rate improved when erroneous decisions were differentially negatively reinforced. However, classification accuracy was not affected by differential negative reinforcement

Keywords

Receiver Operating Characteristic Curve Knowledge Discovery Learning Rate Classification Performance Epidemiologic Surveillance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • John H. Holmes
    • 1
  1. 1.Center for Clinical Epidemiology and BiostatisticsUniversity of Pennsylvania School of MedicineUSA

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