Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making
Abstract The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this...
| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Nature Portfolio
2023-08-01
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| coleção: | Scientific Reports |
| Acesso em linha: | https://doi.org/10.1038/s41598-023-40661-0 |