Deriving the expected utility of a predictive model when the utilities are uncertain

Cooper, GF, Visweswaran, S. Deriving the expected utility of a predictive model when the utilities are uncertain. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Oct 2005) 161-5. PMID: 16779022 PMCID: PMC1560537

Predictive models are often constructed from clinical databases with the goal of eventually helping make better clinical decisions. Evaluating models using decision theory is therefore natural. When constructing a model using statistical and machine learning methods, however, we are often uncertain about precisely how the model will be used. Thus, decision-independent measures of classification performance, such as the area under an ROC curve, are popular. As a complementary method of evaluation, we investigate techniques for deriving the expected utility of a model under uncertainty about the model's utilities. We demonstrate an example of the application of this approach to the evaluation of two models that diagnose coronary artery disease.

Publication Year: 
2005
Publication Credits: 
Gregory F. Cooper, M.D., Ph.D.
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