Summary
Objectives:
The purpose of this communication is to demonstrate the use of “information graphs”
as a means of characterizing diagnostic test performance.
Methods:
Basic concepts in information theory allow us to quantify diagnostic uncertainty
and diagnostic information. Given the probabilities of the diagnoses that can explain
a patient’s condition, the entropy of that distribution is a measure of our uncertainty
about the diagnosis. The relative entropy of the posttest probabilities with respect
to the pretest probabilities quantifies the amount of information gained by diagnostic
testing. Mutual information is the expected value of relative entropy and, hence,
provides a measure of expected diagnostic information. These concepts are used to
derive formulas for calculating diagnostic information as a function of pretest probability
for a given pair of test operating characteristics.
Results:
Plots of diagnostic information as a function of pretest probability are constructed
to evaluate and compare the performance of three tests commonly used in the diagnosis
of coronary artery disease. The graphs illustrate the critical role that the pretest
probability plays in determining diagnostic test information.
Conclusions:
Information graphs summarize diagnostic test performance and offer a way to evaluate
and compare diagnostic tests.
Keywords
Diagnostic tests - entropy - information theory
