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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Related Experiment Video

Updated: Nov 11, 2025

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
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Using information theory to optimize a diagnostic threshold to match physician-ordering practice.

Mark A Zaydman1, Jonathan R Brestoff1, Ronald Jackups1

  • 1Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA.

Journal of Biomedical Informatics
|March 26, 2021
PubMed
Summary

Information theory offers a data-driven method to optimize diagnostic test thresholds, improving test value and reducing uncertainty in laboratory medicine. This approach provides more meaningful insights than traditional accuracy metrics.

Keywords:
Clinical laboratory techniquesDiagnostic errorsInformation theoryMedical informaticsMedical information sciences

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Area of Science:

  • Laboratory Medicine
  • Medical Diagnostics
  • Information Theory

Background:

  • Clinicians utilize laboratory tests to reduce diagnostic uncertainty.
  • Information theory can quantify how much a test result reduces uncertainty.
  • Evaluating diagnostic test thresholds is crucial for accurate clinical decisions.

Purpose of the Study:

  • To apply information theory to optimize diagnostic test thresholds.
  • To compare information theoretic approaches with conventional methodologies.
  • To evaluate the heparin/PF4 immunoassay (PF4 ELISA) as a case study.

Main Methods:

  • Compared conventional and information theoretic approaches for optimizing PF4 ELISA thresholds.
  • Utilized laboratory data including PF4 ELISA and serotonin release assay (SRA) results.
  • Analyzed performance under idealized (50% pretest probability) and realistic (2.4% pretest probability) conditions.

Main Results:

  • Under ideal conditions, both methods yielded similar thresholds (OD > 0.79).
  • Under realistic conditions, information theory suggested a higher threshold (OD > 1.5) compared to conventional methods (OD > 0.6).
  • Increasing the diagnostic threshold improved global information value and positive test value while minimally impacting negative test value.

Conclusions:

  • The current FDA-approved cutoff for PF4 ELISA (OD > 0.4) is overly permissive, reducing test value and increasing diagnostic noise.
  • The information theoretic approach provides a scalable, data-driven strategy for optimizing laboratory test value.
  • Information theory offers more meaningful measures of test value than traditional accuracy-based metrics.