Constructing a Risk Prediction Model for Intraoperative Hypothermia in Children Under General Anesthesia

The study first clarified the overall incidence and influencing factors of IOH in children through meta-analysis, combined with expert consultation to determine potential predictive variables, and developed a data questionnaire for this study. The second part of the study will collect data prospectively, use four algorithms in machine learning, including logistic regression, decision tree, random forest, and support vector machine, to build an excellent intraoperative hypothermia risk prediction model, and conduct internal verification of the model. The third part of the study prospectively collects sample data to complete external verification of time period and space. The constructed model still has good predictive ability in the new data set. Finally, some models are visualized to form intuitive and easy-to-understand charts or interfaces.