Semi-automatic learning of simple diagnostic scores utilizing complexity measures

Objective: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semiautomatic 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 ...

• Type: Journal
• Year: 2006
• Where: ARTMED
• Authors: Martin Atzmüller, Joachim Baumeister, Frank Puppe
• Keywords: ARTMED 2006, Diagnostic Scores, Domain Specialist, Learning Methods