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

ABSTRACT: Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest for an accurate and efficient method for early DIC prediction is of paramount importance. Candidate predictors encompassed demographics, comorbidities, laboratory findings, and therapy strategies. A stepwise logistic regression model was employed to select the features included in the final model. Six machine learning algorithms-logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), decision tree (DT), and k-nearest neighbor (KNN)-were employed to construct predictive models for DIC in critically ill children. Models were then evaluated by using area under the curve (AUC), accuracy, specif

SUBMITTER: Zhou J 

PROVIDER: S-EPMC11965335 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature