Machine learning compared with rule‐in/rule‐out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations

OBJECTIVE: Computerized decision‐support tools may improve diagnosis of acute myocardial infarction (AMI) among patients presenting with chest pain at the emergency department (ED). The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high‐sensitivity...

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Detalles Bibliográficos
Autores principales: Björkelund, Anders, Ohlsson, Mattias, Lundager Forberg, Jakob, Mokhtari, Arash, Olsson de Capretz, Pontus, Ekelund, Ulf, Björk, Jonas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Cardiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7984484/
https://www.ncbi.nlm.nih.gov/pubmed/33778804
http://dx.doi.org/10.1002/emp2.12363

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7984484/
https://www.ncbi.nlm.nih.gov/pubmed/33778804
http://dx.doi.org/10.1002/emp2.12363

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