A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation

Abstract The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality in this vulnerable population. A retrospe...

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Bibliographic Details
Main Authors: Zhen-nan Yuan, Yu-juan Xue, Hai-jun Wang, Shi-ning Qu, Chu-lin Huang, Hao Wang, Hao Zhang, Min-ze Zhang, Xue-zhong Xing
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Machine learning
Acute pulmonary embolism
In-hospital mortality
Cancer
Online Access:https://doi.org/10.1038/s41598-025-02072-1
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https://doi.org/10.1038/s41598-025-02072-1

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