Fig. 4 | Scientific Reports

Fig. 4

From: Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children

Fig. 4

The partial dependence plots of the XGB model based on SHAP. A-P show how the APTT, AST, D-dimer, INR, PLT count, PT, systolic pressure, TT, age, calcium, vasoactive agents, NSAIDs, anticoagulants, sepsis, leukemia, and renal insufficiency affects the output of the XGB prediction model respectively. As the SHAP value exceeds zero, it indicated a promoting effect on the DIC risk. Abbreviations: APTT, activated partial thromboplastin time; AST, aspartate transaminase; INR, international normalized ratio; PLT, platelet count; PT, prothrombin time; TT, thrombin time; NSAIDs, nonsteroidal anti-inflammatory drugs.

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