A software tool for applying Bayes' theorem in medical diagnostics.

ABSTRACT:

Background

In medical diagnostics, estimating post-test or posterior probabilities for disease, positive and negative predictive values, and their associated uncertainty is essential for patient care.

Objective

The aim of this work is to introduce a software tool developed in the Wolfram Language for the parametric estimation, visualization, and comparison of Bayesian diagnostic measures and their uncertainty.

Methods

This tool employs Bayes' theorem to estimate positive and negative predictive values and posterior probabilities for the presence and absence of a disease. It estimates their standard sampling, measurement, and combined uncertainty, as well as their confidence intervals, applying uncertainty propagation methods based on first-order Taylor series approxim

SUBMITTER: Chatzimichail T 

PROVIDER: S-EPMC11662465 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature