Chatzimichail Theodora, Hatjimihail Aristides T
Hellenic Complex Systems Laboratory, Kostis Palamas 21, 66131, Drama, Greece.
BMC Med Inform Decis Mak. 2024 Dec 21;24(1):399. doi: 10.1186/s12911-024-02721-x.
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.
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.
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 approximations. It employs normal, lognormal, and gamma distributions.
The software generates plots and tables of the estimates to support clinical decision-making. An illustrative case study using fasting plasma glucose data from the National Health and Nutrition Examination Survey (NHANES) demonstrates its application in diagnosing diabetes mellitus. The results highlight the significant impact of measurement uncertainty on Bayesian diagnostic measures, particularly on positive predictive value and posterior probabilities.
The software tool enhances the estimation and facilitates the comparison of Bayesian diagnostic measures, which are critical for medical practice. It provides a framework for their uncertainty quantification and assists in understanding and applying Bayes' theorem in medical diagnostics.
在医学诊断中，估计疾病的检验后概率或后验概率、阳性和阴性预测值及其相关不确定性对于患者护理至关重要。
这项工作的目的是介绍一种用Wolfram语言开发的软件工具，用于贝叶斯诊断测量及其不确定性的参数估计、可视化和比较。
该工具采用贝叶斯定理来估计疾病存在和不存在时的阳性和阴性预测值以及后验概率。它应用基于一阶泰勒级数近似的不确定性传播方法来估计它们的标准抽样、测量和组合不确定性，以及它们的置信区间。它采用正态分布、对数正态分布和伽马分布。
该软件生成估计值的图表和表格，以支持临床决策。一个使用来自国家健康和营养检查调查（NHANES）的空腹血糖数据的说明性案例研究展示了其在诊断糖尿病中的应用。结果突出了测量不确定性对贝叶斯诊断测量的重大影响，特别是对阳性预测值和后验概率的影响。
该软件工具增强了贝叶斯诊断测量的估计，并便于其比较，这对医学实践至关重要。它为其不确定性量化提供了一个框架，并有助于在医学诊断中理解和应用贝叶斯定理。
