Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model

Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model

Abstract Gastrointestinal bleeding (GIB) occurs more frequently in cardiovascular patients than in the general population, significantly affecting morbidity and mortality. However, existing predictive models often lack sufficient accuracy and interpretability. We developed an interpretable and pract...

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Bibliographic Details
Main Authors: Yahui Li, Xujie Wang, Xuhui Liu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Gastrointestinal hemorrhage
Cardiovascular disease
Interpretability
Machine learning
XGBoost
Online Access:https://doi.org/10.1038/s41598-025-10906-1
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https://doi.org/10.1038/s41598-025-10906-1

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