Machine Learning Models for Predicting Post-Hepatectomy Liver Failure: A Systematic Review
<b>Background and Objectives:</b> Post-hepatectomy liver failure (PHLF) remains the leading cause of mortality following hepatic resection, with reported incidence rates ranging from 1.2% to 32%. Traditional scoring systems such as the Child–Pugh score, Model for End-Stage Liver Disease (MELD), and...
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| Autores principales: | , , , , , |
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| Formato: | Artigo |
| Lenguaje: | Inglês |
| Publicado: |
MDPI AG
2026-05-01
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| Colección: | AI |
| Materias: | |
| Acceso en línea: | https://www.mdpi.com/2673-2688/7/5/166 |
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