Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain

The pilot study results were encouraging. The data-driven BAYES score approach could learn from frequency counting, make fast and accurate decisions, and explain its reasoning. The computer replicated these aspects of diagnostic expertise. More research is needed to reproduce and extend these finding to larger diverse patient populations.

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Publication Details

Journal
Diagnosis
Published
2024-10-24
DOI
https://doi.org/10.1515/dx-2024-0049
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.35
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article

Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain

Mark W. Perlin, Yves-Dany Accilien
1 citations
Diagnosis
Machine Learning in Healthcare
0.35
article

Bayesian intelligence for medical diagnosis: a pilot study on patient disposition for emergency medicine chest pain

Mark W. Perlin, Yves-Dany Accilien
article en
1 citations

Abstract

The pilot study results were encouraging. The data-driven BAYES score approach could learn from frequency counting, make fast and accurate decisions, and explain its reasoning. The computer replicated these aspects of diagnostic expertise. More research is needed to reproduce and extend these finding to larger diverse patient populations.

DiagnosisVol. 12(1)
WellStar Health System (US), Cybergenetics (United States) (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 32%
Machine Learning in Healthcare
0.35
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