Hypoxemia prediction model based on XGBoost during sedation for gastrointestinal endoscopy
IntroductionHypoxemia is the most common complication of sedated gastrointestinal endoscopy and can lead to serious consequences. Predicting and preventing hypoxemia remains challenging. Accurate prediction using integrated clinical data and artificial intelligence shows great potential. This study...
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| Principais autores: | , , , , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Frontiers Media S.A.
2026-01-01
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| coleção: | Frontiers in Medicine |
| Assuntos: | |
| Acesso em linha: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1714512/full |
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