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A Review of the Application of Information Theory to Clinical Diagnostic Testing

Department of Internal Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
Entropy 2020, 22(1), 97; https://doi.org/10.3390/e22010097
Received: 12 October 2019 / Revised: 8 January 2020 / Accepted: 9 January 2020 / Published: 14 January 2020
(This article belongs to the Special Issue Applications of Information Theory to Epidemiology)
The fundamental information theory functions of entropy, relative entropy, and mutual information are directly applicable to clinical diagnostic testing. This is a consequence of the fact that an individual’s disease state and diagnostic test result are random variables. In this paper, we review the application of information theory to the quantification of diagnostic uncertainty, diagnostic information, and diagnostic test performance. An advantage of information theory functions over more established test performance measures is that they can be used when multiple disease states are under consideration as well as when the diagnostic test can yield multiple or continuous results. Since more than one diagnostic test is often required to help determine a patient’s disease state, we also discuss the application of the theory to situations in which more than one diagnostic test is used. The total diagnostic information provided by two or more tests can be partitioned into meaningful components.
Keywords: entropy; information theory; multiple diagnostic tests; mutual information; relative entropy entropy; information theory; multiple diagnostic tests; mutual information; relative entropy
MDPI and ACS Style

Benish, W.A. A Review of the Application of Information Theory to Clinical Diagnostic Testing. Entropy 2020, 22, 97.

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