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 La...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02721-x |
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| author | Theodora Chatzimichail Aristides T. Hatjimihail |
| author_facet | Theodora Chatzimichail Aristides T. Hatjimihail |
| author_sort | Theodora Chatzimichail |
| collection | DOAJ |
| description | 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 approximations. It employs normal, lognormal, and gamma distributions. Results 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. Conclusion 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. |
| format | Article |
| id | doaj-art-135baaf34db94783ae03bc6ec61d9289 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-135baaf34db94783ae03bc6ec61d92892025-02-02T12:27:52ZengBMCBMC Medical Informatics and Decision Making1472-69472024-12-0124112510.1186/s12911-024-02721-xA software tool for applying Bayes' theorem in medical diagnosticsTheodora Chatzimichail0Aristides T. Hatjimihail1Hellenic Complex Systems LaboratoryHellenic Complex Systems LaboratoryAbstract 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 approximations. It employs normal, lognormal, and gamma distributions. Results 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. Conclusion 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.https://doi.org/10.1186/s12911-024-02721-xBayesian diagnosisBayes’ theoremPrevalencePrior probabilityPost-test probabilityPosterior probability |
| spellingShingle | Theodora Chatzimichail Aristides T. Hatjimihail A software tool for applying Bayes' theorem in medical diagnostics BMC Medical Informatics and Decision Making Bayesian diagnosis Bayes’ theorem Prevalence Prior probability Post-test probability Posterior probability |
| title | A software tool for applying Bayes' theorem in medical diagnostics |
| title_full | A software tool for applying Bayes' theorem in medical diagnostics |
| title_fullStr | A software tool for applying Bayes' theorem in medical diagnostics |
| title_full_unstemmed | A software tool for applying Bayes' theorem in medical diagnostics |
| title_short | A software tool for applying Bayes' theorem in medical diagnostics |
| title_sort | software tool for applying bayes theorem in medical diagnostics |
| topic | Bayesian diagnosis Bayes’ theorem Prevalence Prior probability Post-test probability Posterior probability |
| url | https://doi.org/10.1186/s12911-024-02721-x |
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