用机器学习预测儿童脑外伤患者死亡率和功能评分，提升临床决策效率。
Predicting Mortality and Functional Status Scores of Traumatic Brain Injury Patients using Supervised Machine Learning
- 基于18种模型分析300名患儿临床数据，筛选关键风险因素
- 逻辑回归与额外树模型在死亡预测上准确率高，线性回归最准预测功能评分
- 特征筛选后模型更简洁易懂，适合临床部署
创伤性脑损伤（TBI）是重大公共卫生挑战，常导致死亡或长期残疾。预测死亡率和功能状态量表（FSS）评分有助于优化治疗策略并支持临床决策。本研究利用来自科罗拉多大学医学院的真实世界数据集，对300名儿科TBI患者应用监督学习方法，涵盖人口统计、受伤机制及住院结果等临床特征。评估了18种模型用于死亡预测，13种模型用于FSS评分预测，性能指标包括准确率、ROC AUC、F1分数和均方误差。逻辑回归和额外树模型在死亡预测中表现优异，线性回归在FSS评分预测中表现最佳。通过特征选择将103个临床变量精简为关键变量，提升了模型效率与可解释性。研究表明，机器学习可有效识别高危患者，支持个性化干预，展现数据驱动分析在改善TBI诊疗中的潜力，并具备集成至临床工作流的可行性。
原文摘要 · Abstract (English)
Traumatic brain injury (TBI) presents a significant public health challenge, often resulting in mortality or lasting disability. Predicting outcomes such as mortality and Functional Status Scale (FSS) scores can enhance treatment strategies and inform clinical decision-making. This study applies supervised machine learning (ML) methods to predict mortality and FSS scores using a real-world dataset of 300 pediatric TBI patients from the University of Colorado School of Medicine. The dataset captures clinical features, including demographics, injury mechanisms, and hospitalization outcomes. Eighteen ML models were evaluated for mortality prediction, and thirteen models were assessed for FSS score prediction. Performance was measured using accuracy, ROC AUC, F1-score, and mean squared error. Logistic regression and Extra Trees models achieved high precision in mortality prediction, while linear regression demonstrated the best FSS score prediction. Feature selection reduced 103 clinical variables to the most relevant, enhancing model efficiency and interpretability. This research highlights the role of ML models in identifying high-risk patients and supporting personalized interventions, demonstrating the potential of data-driven analytics to improve TBI care and integrate into clinical workflows.
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