Semi-automatic learning of simple diagnostic scores utilizing complexity measures

  • Authors:

  • Martin Atzmueller;Joachim Baumeister;Frank Puppe

  • Affiliations:

  • Department of Computer Science, University of Würzburg, Am Hubland, 97074 Würzburg, Germany;Department of Computer Science, University of Würzburg, Am Hubland, 97074 Würzburg, Germany;Department of Computer Science, University of Würzburg, Am Hubland, 97074 Würzburg, Germany

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2006

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Abstract

Objective: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semi-automatic learning methods can be used to support the domain specialists. They are usually not only interested in the accuracy of the learned knowledge: the understandability and interpretability of the learned models is of prime importance as well. Then, often simple models are more favorable than complex ones. Methods and material: We propose diagnostic scores as a promising approach for the representation of simple diagnostic knowledge, and present a method for inductive learning of diagnostic scores. It can be incrementally refined by including background knowledge. We present complexity measures for determining the complexity of the learned scores. Results: We give an evaluation of the presented approach using a case base from the fielded system SonoConsult. We further discuss that the user can easily balance between accuracy and complexity of the learned knowledge applying the presented measures. Conclusions: We argue that semi-automatic learning methods can support the domain specialist efficiently when building (diagnostic) knowledge systems from scratch. The presented complexity measures allow for an intuitive assessment of the learned patterns. ns.