Intuitive and Axiomatic Arguments for Quantifying Diagnostic Test Performance in Units of Information

Benish, William A.

doi:10.3414/me0627

Cited by 16 publications

(22 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the life sciences, Shannon entropy has been used to measure cellular diversity (12,13) and phylogenetic variation (14), and to model molecular interactions (15). This concept has previously been applied to singleanalyte laboratory testing by Rudolph (16)(17)(18) and, more recently, by Benish (19)(20)(21)(22)(23) and Vollmer (24). However, their approaches have not been widely adopted or disseminated and, in particular, have not been applied to NGS.…”

mentioning

confidence: 98%

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

et al. 2015

View full text Add to dashboard Cite

mentioning

confidence: 98%

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

et al. 2015

View full text Add to dashboard Cite

“…Since these possibilities are equal in both tests, the entropy of the disease is the same. When we have a look at [6].…”

Section: Application and Resultsmentioning

confidence: 99%

Evaluation and Comparison of Diagnostic Test Performance Based on Information Theory

Oruç¹,

Kanca²

2012

STATISTICS

View full text Add to dashboard Cite

A fundamental concept of information theory, relative entropy and mutual information, is directly applicable to evaluation of diagnostic test performance. The aim of this study is to demonstrate how basic concepts in information theory apply to the problem of quantifying major depressive disorder diagnostic test performance. In this study, the performances of the Dexamethasone Suppression Test-DST and the Thyroid-Stimulating Hormone Test-TSH, two of the diagnosis tests of Major Depressive Disorder, are evaluated with the method of Information Theory. The amount of information gained by performing a diagnostic test can be quantified by calculating the relative entropy between the posttest and pretest probability distributions. And also demonstrates that diagnostic test performance can be quantified as the average amount of information the test result provides about the disease state. It is aimed that this study will hopefully give various points of view to the researchers who want to make research on this subject by explaining how the tests used for the diagnosis of various diseases are evaluated with this way.

show abstract

“…In this and subsequent sections, we explore how to quantify the information provided by diagnostic test results [13-16]. This exploration will necessitate a framework for handling uncertainty at the level of disease probability, and at the level of ranges instead of point estimates of disease probability.…”

Section: Information Theory: the Surprisalmentioning

confidence: 99%

“…The surprisal is defined mathematically as

S (p) = - \log_{2} p

where S is the surprisal and p is the probability of an event occurring. For reasons of convention and practicality [16], the logarithm is taken with base 2, yielding surprisal in units of “bits”. Fig.…”

Section: Information Theory: the Surprisalmentioning

confidence: 99%

“…2B ). This fact can lead to another apparent paradox, in that a clinically “informative” test result may nevertheless have minimal impact on the degree of entropy or uncertainty, as pointed out by Benish [13-16]. For example, one can imagine a test result moving OSA probability from 10% (low likelihood) to 90% (high likelihood), which might substantially influence clinical management (e.g., treatment with CPAP versus no treatment).…”

Section: Information Theory: Entropymentioning

confidence: 99%

See 1 more Smart Citation

Information Theoretic Quantification of Diagnostic Uncertainty

Westover¹,

Eiseman²,

Cash³

et al. 2012

TOMINFOJ

View full text Add to dashboard Cite

Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the use of Bayes’ rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test probability to quantify the change in disease probability incurred by a new test result. However, multiple studies demonstrate physicians’ deficiencies in probabilistic reasoning, especially with unexpected test results. Information theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an alternative framework for diagnostic test interpretation, but is even less familiar to physicians. We have previously addressed one key challenge in the practical application of Bayes theorem: the handling of uncertainty in the critical first step of estimating the pre-test probability of disease. This essay aims to present the essential concepts of information theory to physicians in an accessible manner, and to extend previous work regarding uncertainty in pre-test probability estimation by placing this type of uncertainty within a principled information theoretic framework. We address several obstacles hindering physicians’ application of information theoretic concepts to diagnostic test interpretation. These include issues of terminology (mathematical meanings of certain information theoretic terms differ from clinical or common parlance) as well as the underlying mathematical assumptions. Finally, we illustrate how, in information theoretic terms, one can understand the effect on diagnostic uncertainty of considering ranges instead of simple point estimates of pre-test probability.

show abstract

Intuitive and Axiomatic Arguments for Quantifying Diagnostic Test Performance in Units of Information

Abstract: Mutual information is the best single measure of the ability of a diagnostic test to discriminate among the possible disease states.

Cited by 16 publications

References 24 publications

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

Diagnostic yield of targeted next generation sequencing in various cancer types: An information-theoretic approach

Evaluation and Comparison of Diagnostic Test Performance Based on Information Theory

Information Theoretic Quantification of Diagnostic Uncertainty

Contact Info

Product

Resources

About