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Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.

Shu ZhouZongqing LuYu LiuMinjie WangWuming ZhouXuanxuan CuiJin ZhangWenyan XiaoTianfeng HuaHuaqing ZhuMin Yang
Published in: European journal of medical research (2024)
We developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.
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