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Fig. 4 | European Journal of Medical Research

Fig. 4

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

Fig. 4

The partial dependence plots of the XGboost model based on SHAP. A-P show how the RDW_max, age, MCV_min, Heartrate_mean, Tempc_mean, Resprate_mean, Po2_min, PT_max, MAP, platelet_min, lactate_max, WBC_max, PTT_max, gender and MCHC_min affects the output of the XGBoost prediction model respectively. As the SHAP value exceeds zero, it indicated a promoting effect on the 28-day death risk. RDW=red blood cell distribution width; MCV=mean corpuscular volume; BUN=blood urea nitrogen; MBP=mean blood pressure; WBC=white blood cell; MCHC=mean corpuscular hemoglobin concentration

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