Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients

OBJECTIVES: Identifying high-risk patients is crucial for effective cardiovascular disease (CVD) prevention. It is not known whether electronic health record (EHR)-based machine-learning (ML) models can improve CVD risk stratification compared with a secondary prevention risk score developed from ra...

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Detalles Bibliográficos
Autores principales: Sarraju, Ashish, Ward, Andrew, Chung, Sukyung, Li, Jiang, Scheinker, David, Rodríguez, Fàtima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Cardiac Risk Factors and Prevention
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527119/
https://www.ncbi.nlm.nih.gov/pubmed/34667093
http://dx.doi.org/10.1136/openhrt-2021-001802

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8527119/
https://www.ncbi.nlm.nih.gov/pubmed/34667093
http://dx.doi.org/10.1136/openhrt-2021-001802

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