Cargando…
A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis
Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations be...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572594/ https://www.ncbi.nlm.nih.gov/pubmed/37835877 http://dx.doi.org/10.3390/diagnostics13193135 |
_version_ | 1785120269695713280 |
---|---|
author | Chatzimichail, Theodora Hatjimihail, Aristides T. |
author_facet | Chatzimichail, Theodora Hatjimihail, Aristides T. |
author_sort | Chatzimichail, Theodora |
collection | PubMed |
description | Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus. |
format | Online Article Text |
id | pubmed-10572594 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105725942023-10-14 A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis Chatzimichail, Theodora Hatjimihail, Aristides T. Diagnostics (Basel) Article Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose, and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus. MDPI 2023-10-05 /pmc/articles/PMC10572594/ /pubmed/37835877 http://dx.doi.org/10.3390/diagnostics13193135 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chatzimichail, Theodora Hatjimihail, Aristides T. A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis |
title | A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis |
title_full | A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis |
title_fullStr | A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis |
title_full_unstemmed | A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis |
title_short | A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis |
title_sort | bayesian inference based computational tool for parametric and nonparametric medical diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572594/ https://www.ncbi.nlm.nih.gov/pubmed/37835877 http://dx.doi.org/10.3390/diagnostics13193135 |
work_keys_str_mv | AT chatzimichailtheodora abayesianinferencebasedcomputationaltoolforparametricandnonparametricmedicaldiagnosis AT hatjimihailaristidest abayesianinferencebasedcomputationaltoolforparametricandnonparametricmedicaldiagnosis AT chatzimichailtheodora bayesianinferencebasedcomputationaltoolforparametricandnonparametricmedicaldiagnosis AT hatjimihailaristidest bayesianinferencebasedcomputationaltoolforparametricandnonparametricmedicaldiagnosis |