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基于机器学习的儿童创伤患者的死亡率预测

Alex Deleon¹, Anish Murala², Isabelle Decker¹

¹Long School of Medicine, UT Health San Antonio, San Antonio, TX, United States.

Frontiers in pediatrics

|March 14, 2025

概括

这项研究开发了一种机器学习 (ML) 模型,用于预测儿科创伤患者的死亡率. 该XGBoost模型实现了97.7%的准确性,为早期干预提供了有价值的工具.

科学领域:

医疗信息学医疗信息学
儿科创伤学儿科创伤学
医疗保健中的机器学习

背景情况:

儿科创伤是死亡的一个重要原因.
准确预测死亡率对于及时干预至关重要.
现有的模型可能无法充分利用先进的计算技术.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测儿科创伤患者的死亡率.
确定这一群体中死亡率的关键临床和生理预测因素.
评估不同的ML算法在预测儿科创伤结果方面的性能.

主要方法:

从国家创伤数据库 (NTDB) 中提取了510,381名儿科创伤患者 (≤18岁) 的数据.
一个开发队列 (70%) 用于构建四个ML模型,在一个单独的30%队列中进行验证.
模型性能是使用接收器操作特征曲线 (AUC) 下的面积来评估的.

主要成果:

总体死亡率为1.6% (8,250名儿童中有510,381名儿童).
开发了四种ML模型,AUC从92.7%到97.7%不等.
XGBoost模型表现出最高的性能,AUC达到97.7%和97.7%的准确性.

结论:

机器学习算法可以有效地预测儿科创伤患者的死亡率.
开发的XGBoost模型提供了一个高度准确的预测工具.
在创伤入院时,容易获得的变量足以构建准确的预测模型.

关键词:

机器学习是机器学习.死亡率死亡率儿科儿科儿科儿科预测预测预测预测创伤的创伤创伤的创伤.

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