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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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| Publicado no: | JAMA Netw Open |
|---|---|
| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
American Medical Association
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6991258/ https://ncbi.nlm.nih.gov/pubmed/31922560 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1001/jamanetworkopen.2019.18962 |
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