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Deriving the Expected Utility of a Predictive Model When the Utilities Are Uncertain

By Gregory F. Cooper and Shyam Visweswaran

Abstract

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 a 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

Topics: Article
Publisher: American Medical Informatics Association
OAI identifier: oai:pubmedcentral.nih.gov:1560537
Provided by: PubMed Central
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