ORIGINAL RESEARCH article
Front. Physiol.
Sec. Computational Physiology and Medicine
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1629329
Development and Clinical Application of an Automated Machine Learning-Based Delirium Risk Prediction Model for Emergency Polytrauma Patients
Provisionally accepted- 1The 945th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army, Ya'an, China
- 2The 945th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Ya‘an, China
- 3Ya’an People’s Hospital, Ya'an, China
- 4Yucheng District People’s Hospital of Ya’an, Ya'an, China
- 5Mingshan District People’s Hospital of Ya’an, Ya’an, China
- 6Affiliated Hospital of Ya’an Polytechnic College, Ya’an, China
- 7Ya’an Hospital of Traditional Chinese Medicine, Ya’an, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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.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.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.
Keywords: Delirium, polytrauma, machine learning, predictive model, Explainable artificial intelligence
Received: 16 May 2025; Accepted: 04 Jul 2025.
Copyright: © 2025 Liu, Huang, Li, Xu, Wu, Zhang, Han, Zhang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ming Zhang, The 945th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army, Ya‘an, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.