Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence
Abstract Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We conducted a retrospective cohort study of hosp...
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| Auteurs principaux: | , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Nature Portfolio
2020
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/f597209c04d945e28c2088d6d39041c2 |
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