Development and external validation of a machine learning model for predicting in-hospital mortality in acute liver failure

Key Points

• In-hospital mortality prediction is enhanced by a machine learning model, providing timely support for clinical decisions.
• The model shows promise in predicting mortality with high accuracy, supporting dynamic risk stratification.
• Analysis utilized a machine learning approach validated externally across diverse clinical settings to ensure robustness.
• Overall, this development may enable healthcare providers to better manage patients with acute liver failure, optimizing outcomes.

Abstract