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 YangPublished 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.
Keyphrases
- end stage renal disease
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- clinical practice
- intensive care unit
- peritoneal dialysis
- prognostic factors
- type diabetes
- cardiovascular disease
- patient reported outcomes
- cardiovascular events
- risk assessment
- artificial intelligence
- coronary artery disease
- human health