Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selectio...

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Detalles Bibliográficos
Autores principales: Steele, Andrew J., Denaxas, Spiros C., Shah, Anoop D., Hemingway, Harry, Luscombe, Nicholas M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6118376/
https://www.ncbi.nlm.nih.gov/pubmed/30169498
http://dx.doi.org/10.1371/journal.pone.0202344

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6118376/
https://www.ncbi.nlm.nih.gov/pubmed/30169498
http://dx.doi.org/10.1371/journal.pone.0202344

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