Developing an Interpretable Machine Learning Model to Predict in-Hospital Mortality in Sepsis Patients: A Retrospective Temporal Validation Study.

Shuhe Li,Ruoxu Dou,Xiaodong Song,Ka Yin Lui,Jinghong Xu,Zilu Guo,Xiaoguang Hu,Xiangdong Guan,Changjie Cai

Journal of clinical medicine（2023）

引用 2|浏览11

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摘要

We developed and validated a novel XGBoost-based model and demonstrated significantly improved performance to LR and other scores in predicting the mortality risks of sepsis patients in the hospital using features in the first 24 h.

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关键词

extreme gradient boosting,interpretability,mortality,sepsis,temporal validation

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