ReviewEntropy (Basel, Switzerland)2020
A Review of the Application of Information Theory to Clinical Diagnostic Testing.
Review in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Axioms of Diagnostic Urgency: A Characterization Theorem.Entropy (Basel, Switzerland) · 2026Article
- Association of HbADiabetologia · 2026Article
- Simulated evaluation of large language model stepwise diagnostic reasoning with real-world chest pain encounters and Bayesian networks.BMC medical informatics and decision making · 2026Article
- Information Theory-Guided Detection of Biomarkers Using Programmable Aptamer Arrays.medRxiv : the preprint server for health sciences · 2025Article
- Geometric Insights into the Multivariate Gaussian Distribution and Its Entropy and Mutual Information.Entropy (Basel, Switzerland) · 2023Article
- Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests.Medical & biological engineering & computing · 2022Review
- The injury severity score: an operations perspective.BMC medical research methodology · 2022Article
- Using information theory to optimize a diagnostic threshold to match physician-ordering practice.Journal of biomedical informatics · 2021Article
- Active Learning Performance in Labeling Radiology Images Is 90% Effective.Frontiers in radiology · 2021Article
- Applications of Information Theory to Epidemiology.Entropy (Basel, Switzerland) · 2020Article
- Mutual Information as a Performance Measure for Binary Predictors Characterized by Both ROC Curve and PROC Curve Analysis.Entropy (Basel, Switzerland) · 2020Article
- Establishment and Characterization of an Empirical Biomarker SS/PV-ROC Plot Using Results of the UBCEntropy (Basel, Switzerland) · 2020Article
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Authors and funding
1 author.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.