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Fouad Trad¹, Bassel Isber², Ryan Yammine³

¹Electrical and Computer Engineering Department, American University of Beirut, Beirut, Lebanon. fat10@mail.aub.edu.

Scientific reports

|July 2, 2025

概括

机器学习模型准确地预测了老年患者的关节骨折手术后30天的死亡风险. 这些算法利用手术前和手术后的数据,为改善患者护理和风险分层提供临床可用的见解.

科学领域:

老年人手术是一门老年人手术.
医疗保健中的机器学习
手术结果研究研究.

背景情况:

老年人部骨折与高死亡率有关,这构成了重大的临床挑战.
现有的风险预测模型可能无法完全捕捉关节骨折后死亡率的复杂性.
机器学习的进步为更准确的风险评估提供了潜力.

研究的目的:

开发和评估机器学习模型,用于预测接受关节骨折手术的老年患者的30天死亡风险.
将使用手术前数据的模型与包含手术前和手术后因素的模型进行比较.
确定关键预测因素,并确保模型可用于临床应用的解释性.

主要方法:

利用了国家外科质量改善计划 (NSQIP 2012-2017) 中62,492名患者的数据.
开发和优化各种机器学习算法 (例如,AdaBoost,CatBoost) 使用十倍交叉验证和超参数调整.
采用特征选择和可解释性技术 (SHAP) 来推导经济和临床可信的模型.

主要成果:

最好的术前模型 (AdaBoost) 的AUC为0.792 (29个特征),最好的术后模型 (CatBoost) 的AUC为0.885 (45个特征).
具有较少特征的优化模型保持了高性能:术前AUC为0.725 (8个特征),术后AUC为0.8529 (6个特征).
可解释性分析证实,学习模式具有临床相关性.

结论:

机器学习模型可以有效地预测老年人部骨折手术后的30天死亡风险.
结合手术后数据的模型显示出卓越的预测性能.
开发的,可解释的模型具有有限的特征集,在临床环境中非常适用于风险分层和决策.

关键词:

人工智能可解释性部骨折部骨折部骨折机器学习是机器学习.术后死亡风险评分术后死亡风险评分术前死亡风险评分,以及手术前死亡风险评分.

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