Development and clinical application of an automated machine learning-based delirium risk prediction model for emergency polytrauma patients.

Objective: To address the limitations of conventional delirium prediction models in emergency polytrauma care, this study developed an interpretable machine learning (ML) framework incorporating trauma-specific biomarkers and advanced optimization algorithms for risk stratification of delirium in emergency polytrauma patients. Methods: This multi-center retrospective observational cohort study was conducted across six hospitals in the Ya'an region. A total of 956 polytrauma patients admitted between January 2020 and December 2024 were enrolled, complying with the American Association for the Surgery of Trauma (AAST) diagnostic criteria for polytrauma. Demographic, clinical (e.g., Glasgow Coma Scale [GCS], Injury Severity Score [ISS]), and laboratory data (e.g., fibrin degradation products [FDP], lactate) were systematically collected. To address high-dimensional clinical heterogeneity, an Improved Flood Algorithm (IFLA)-enhanced with sine mapping initialization and Cauchy mutation perturbations-was integrated into an automated machine learning (AutoML) framework for simultaneous feature selection and hyperparameter optimization. Model performance was benchmarked against conventional algorithms (logistic regression [LR], support vector machine [SVM], extreme gradient boosting [XGBoost], LightGBM) using five-fold cross-validation. The SHapley Additive exPlanations (SHAP) framework quantified predictor contributions, and a MATLAB-based clinical decision support system (CDSS) was implemented for real-time risk stratification. Results: The improved algorithm significantly outperformed other algorithms on 12 standard test functions. The automated machine learning (AutoML) model achieved ROC-AUC and PR-AUC values of 0.9690 and 0.9611, respectively, on the training set, and 0.8929 and 0.8487, respectively, on the test set, both significantly higher than those of four other prediction models. The AutoML model identified 5 important features: Glasgow Coma Scale (GCS) score, lactate level, Clinical Frailty Scale (CFS), body mass index (BMI), and fibrin degradation products (FDP). The decision support system demonstrated clinical utility with net benefit across risk thresholds. Conclusion: This study provides a trauma-specific, interpretable ML tool that integrates GCS scoring and dynamic biomarker monitoring, enabling early delirium risk identification in emergency polytrauma. The framework demonstrates feasibility for integration into clinical workflows to improve trauma care quality.