Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

BACKGROUND: Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patient...

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Detalles Bibliográficos
Autores principales: Ahmad, Tariq, Lund, Lars H., Rao, Pooja, Ghosh, Rohit, Warier, Prashant, Vaccaro, Benjamin, Dahlström, Ulf, O'Connor, Christopher M., Felker, G. Michael, Desai, Nihar R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015420/
https://www.ncbi.nlm.nih.gov/pubmed/29650709
http://dx.doi.org/10.1161/JAHA.117.008081

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6015420/
https://www.ncbi.nlm.nih.gov/pubmed/29650709
http://dx.doi.org/10.1161/JAHA.117.008081

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