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机器学习模型用于预测烧伤患者的住院死亡率

Samet Şahin¹, Burak Yavuz², Onur Karaca³

¹Department of General Surgery, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey.

Annali italiani di chirurgia

|August 18, 2025

概括

机器学习模型准确地预测烧伤患者的住院死亡率. 后勤回归和随机森林显示了改善烧伤护理临床决策的巨大潜力.

科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
烧伤护理研究研究

背景情况:

在烧伤患者的住院死亡率预测对于有效的临床管理至关重要.
机器学习 (ML) 为复杂的健康数据提供了先进的分析能力.

研究的目的:

开发和评估机器学习模型,用于预测烧伤患者的住院死亡率.
确定这一患者群体中死亡率的关键预测因素.

主要方法:

一项回顾性队列研究分析了218名烧伤患者 (2015-2020) 的数据.
七个ML模型 (物流回归,随机森林,SVM,决策树,KNN,天真贝叶斯,梯度提升) 被训练和评估.
关键变量包括人口统计,烧伤特征和炎症标志物.

主要成果:

整体住院死亡率为18.8%.
后勤回归实现了最高的ROC-AUC (0.906),而随机森林显示了最高的准确性 (90.9%) 和召回 (97.2%).
K-最近的邻居显示出优越的回忆力 (99.0%).

结论:

机器学习模型,特别是逻辑回归和随机森林,在预测烧伤患者死亡率方面是有效的.
这些发现支持使用ML进行数据驱动的预后和个性化治疗.
为了更广泛的适用性,建议进行多中心验证.

关键词:

烧伤/死亡情况机器学习是机器学习.风险评估风险评估风险评估

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