Applying Bayesian reasoning to electrocardiogram interpretation
Copyright © 2023 Elsevier Inc. All rights reserved..
Electrocardiograms (ECGs) are a cornerstone in cardiac care. Traditional statistical metrics like sensitivity and specificity are commonly used for diagnostic evaluations but are limited when applied in clinical settings due to their inability to incorporate pre-test likelihoods or individual patient context. Traditional diagnostic metrics do not provide a complete picture in clinical scenarios. Bayesian reasoning allows for a more nuanced approach, integrating pre-test probabilities and individual patient context to produce more accurate post-test probabilities. This was demonstrated through Bayesian analysis of four clinical cases. Bayesian reasoning enhances diagnostic accuracy and personalizes patient care by integrating prior probabilities into diagnostic decision-making. This shift toward Bayesian reasoning is crucial for improving patient outcomes in the era of evidence-based medicine.
| Errataetall: |
CommentIn: J Electrocardiol. 2023 Nov-Dec;81:300-302. - PMID 37951822 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:81 |
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| Enthalten in: |
Journal of electrocardiology - 81(2023) vom: 18. Nov., Seite 295-299 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
de Alencar Neto, José Nunes [VerfasserIn] |
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| Links: |
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| Themen: |
Bayes theorem |
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| Anmerkungen: |
Date Completed 04.12.2023 Date Revised 18.12.2023 published: Print-Electronic CommentIn: J Electrocardiol. 2023 Nov-Dec;81:300-302. - PMID 37951822 Citation Status MEDLINE |
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| doi: |
10.1016/j.jelectrocard.2023.10.006 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 520 | |a Electrocardiograms (ECGs) are a cornerstone in cardiac care. Traditional statistical metrics like sensitivity and specificity are commonly used for diagnostic evaluations but are limited when applied in clinical settings due to their inability to incorporate pre-test likelihoods or individual patient context. Traditional diagnostic metrics do not provide a complete picture in clinical scenarios. Bayesian reasoning allows for a more nuanced approach, integrating pre-test probabilities and individual patient context to produce more accurate post-test probabilities. This was demonstrated through Bayesian analysis of four clinical cases. Bayesian reasoning enhances diagnostic accuracy and personalizes patient care by integrating prior probabilities into diagnostic decision-making. This shift toward Bayesian reasoning is crucial for improving patient outcomes in the era of evidence-based medicine | ||
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