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Recurrent neural networks for early detection of heart failure from longitudinal electronic health record data: Implications for temporal modeling with respect to time before diagnosis, data density, data quantity and data type
BACKGROUND: We determined the impact of data volume and diversity, and training conditions on recurrent neural network (RNN) methods compared to traditional machine learning methods. METHODS AND RESULTS: Using longitudinal electronic health record (EHR) data, we assessed the relative performance of...
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| Publicado en: | Circ Cardiovasc Qual Outcomes |
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| Main Authors: | , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
2019
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| Assuntos: | |
| Acceso en liña: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6814386/ https://ncbi.nlm.nih.gov/pubmed/31610714 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1161/CIRCOUTCOMES.118.005114 |
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