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Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning
PURPOSE: This study sought to develop models with good identification for adverse outcomes in patients with heart failure (HF) and find strong factors that affect prognosis. PATIENTS AND METHODS: A total of 5004 qualifying cases were selected, among which 498 cases had adverse outcomes and 4506 case...
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| Publicado no: | Risk Manag Healthc Policy |
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| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Dove
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8206455/ https://ncbi.nlm.nih.gov/pubmed/34149290 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2147/RMHP.S310295 |
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