A machine learning model for predicting the probability of hypothermia in trauma patients: a multi-center retrospective cohort study.

Hypothermia, a component of the "lethal triad," commonly complicates the condition of critically injured trauma patients, thereby substantially elevating the risk of mortality. This study develop and evaluate a dynamic warning system based on non-invasive features, aimed at predicting the likelihood of hypothermia occurring in trauma patients within the next hour. 462 patients from the eICU database were selected on the basis of meeting the inclusion criteria, and 19 non-invasive and 17 invasive features were extracted. Five classic machine learning methods were employed to develop dynamic early warning model for hypothermia based on various observation windows, with multi-center data used for model validation. The shapley additive explanations (SHAP) algorithm was utilized to analyze the interpretability of the model, and ablation experiments were conducted to further evaluate the contribution of significant feature to the prediction performance. The AUC values of the optimal models based on non-invasive features in the same test set are 0.838. When using cross-hospital data as the validation set, the highest AUC values for the same models based on non-invasive features decrease by only 0.015. In addition, ablation experiments reveal that the model's AUC exhibited a 0.010 improvement when the three most influential invasive features were incorporated into the non-invasive feature set. The results show that machine learning models have shown significant potential in predicting hypothermia through the utilization of solely non-invasive features.