Literature DB >> 22368178

The channel capacity of a diagnostic test as a function of test sensitivity and test specificity.

William A Benish1.   

Abstract

We apply the information theory concept of "channel capacity" to diagnostic test performance and derive an expression for channel capacity in terms of test sensitivity and test specificity. The expected value of the amount of information a diagnostic test will provide is equal to the "mutual information" between the test result and the disease state. For the case in which only two test results and two disease states are considered, mutual information, I(D;R), is a function of sensitivity, specificity, and the pretest probability of disease. The channel capacity of the test is the maximal value of I(D;R) for a given sensitivity and specificity. After deriving an expression for I(D;R) in terms of sensitivity, specificity, and pretest probability, we solve for the value of pretest probability that maximizes I(D;R). Channel capacity is obtained by using this value of pretest probability to calculate I(D;R). Channel capacity provides a convenient and meaningful single parameter measure of diagnostic test performance. It quantifies the upper limit of the amount of information a diagnostic test can be expected to provide about a patient's disease state.
© The Author(s) 2011.

Entities:  

Keywords:  Diagnostic tests; channel capacity; information theory

Mesh:

Year:  2012        PMID: 22368178     DOI: 10.1177/0962280212439742

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  4 in total

1.  Beyond kappa: an informational index for diagnostic agreement in dichotomous and multivalue ordered-categorical ratings.

Authors:  Alberto Casagrande; Francesco Fabris; Rossano Girometti
Journal:  Med Biol Eng Comput       Date:  2020-11-03       Impact factor: 2.602

2.  Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis.

Authors:  Gareth Hughes; Jennifer Kopetzky; Neil McRoberts
Journal:  Entropy (Basel)       Date:  2020-08-26       Impact factor: 2.524

Review 3.  A Review of the Application of Information Theory to Clinical Diagnostic Testing.

Authors:  William A Benish
Journal:  Entropy (Basel)       Date:  2020-01-14       Impact factor: 2.524

Review 4.  Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests.

Authors:  Alberto Casagrande; Francesco Fabris; Rossano Girometti
Journal:  Med Biol Eng Comput       Date:  2022-02-23       Impact factor: 2.602

  4 in total

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