Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes

IMPORTANCE: Accurate risk stratification of patients with heart failure (HF) is critical to deploy targeted interventions aimed at improving patients’ quality of life and outcomes. OBJECTIVES: To compare machine learning approaches with traditional logistic regression in predicting key outcomes in p...

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Detalles Bibliográficos
Autores principales: Desai, Rishi J., Wang, Shirley V., Vaduganathan, Muthiah, Evers, Thomas, Schneeweiss, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2020
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991258/
https://www.ncbi.nlm.nih.gov/pubmed/31922560
http://dx.doi.org/10.1001/jamanetworkopen.2019.18962

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6991258/
https://www.ncbi.nlm.nih.gov/pubmed/31922560
http://dx.doi.org/10.1001/jamanetworkopen.2019.18962

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