Intuitive and axiomatic arguments for quantifying diagnostic test performance in units of information.
OBJECTIVE
Mutual information is a fundamental concept of information theory that quantifies the expected value of the amount of information that diagnostic testing provides about a patient's disease state. The purpose of this report is to provide both intuitive and axiomatic descriptions of mutual information and, thereby, promote the use of this statistic as a measure of diagnostic test performance.


METHODS
We derive the mathematical expression for mutual information from the intuitive assumption that diagnostic information is the average amount that diagnostic testing reduces our surprise upon ultimately learning a patient's diagnosis. This concept is formalized by defining "surprise" as the surprisal, a function that quantifies the unlikelihood of an event. Mutual information is also shown to be the only function that conforms to a set of axioms which are reasonable requirements of a measure of diagnostic information. These axioms are related to the axioms of information theory used to derive the expression for entropy.


RESULTS
Both approaches to defining mutual information lead to the known relationship that mutual information is equal to the pretest uncertainty of the disease state minus the expected value of the posttest uncertainty of the disease state. Mutual information also has the property of being additive when a test provides information about independent health problems.


CONCLUSIONS
Mutual information is the best single measure of the ability of a diagnostic test to discriminate among the possible disease states.
Benish
W A
WA
Louis Stokes Cleveland Department of Veterans Affairs Medical Center and Case Western Reserve University, Ohio, USA. wab4@cwru.edu
D000465:Algorithms; D003955:Diagnostic Tests, Routine / Q000592:standards*; D007257:Information Theory