Machine learning for prediction of delirium in patients with extensive burns after surgery.

ABSTRACT:

Aims

Machine learning-based identification of key variables and prediction of postoperative delirium in patients with extensive burns.

Methods

Five hundred and eighteen patients with extensive burns who underwent surgery were included and randomly divided into a training set, a validation set, and a testing set. Multifactorial logistic regression analysis was used to screen for significant variables. Nine prediction models were constructed in the training and validation sets (80% of dataset). The testing set (20% of dataset) was used to further evaluate the model. The area under the receiver operating curve (AUROC) was used to compare model performance. SHapley Additive exPlanations (SHAP) was used to interpret the best one and to externally validate it in another large tert

SUBMITTER: Ren Y 

PROVIDER: S-EPMC10493655 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature