使用综合老年评估预测老年人死亡率:传统统计和机器学习方法的比较研究
Esin Avsar Kucukkurt1, Esra Tokur Sonuvar2, Dilek Yapar3
1Department of Internal Medicine, Faculty of Medicine, Akdeniz University, Antalya 07070, Türkiye.
Diagnostics (Basel, Switzerland)
|October 16, 2025
概括
综合老年评估 (CGA) 参数有效预测老年人的死亡率. 功能衰退和炎症标志物是关键预测因素,仅次于时间年龄.
科学领域:
- 老年学是指老年学的学科.
- 生物统计学 生物统计学
- 医疗保健中的人工智能
背景情况:
- 老年人是一个不断增长的人口,需要准确的死亡风险评估.
- 综合老年评估 (CGA) 是一种用于评估老年人的健康状况的多维工具.
- 在这个人群中预测全因死亡率对于积极的医疗保健规划至关重要.
研究的目的:
- 评估CGA参数对老年人全因死亡率的预测能力.
- 将传统的统计方法与机器学习 (ML) 方法进行比较,用于死亡率预测.
- 确定关键的CGA变量,这些变量是死亡率的重要预测因素.
主要方法:
- 分析了来自大学医院门诊的1974名老年人的队列.
- 评估了96个CGA变量,涵盖功能,营养,认知和炎症状态.
- 考克斯回归和6个ML算法 (包括人工神经网络和物流回归) 用于预测建模.
主要成果:
- 在平均617天的随访期间,21.7%的参与者死亡.
- 较低的劳顿IADL分数,无意的体重减轻,步行速度减慢和C反应蛋白升高是一致的死亡率预测因素.
- 人工神经网络实现了最高的预测性能 (AUC = 0.970),超过了后勤回归 (AUC = 0.851).
结论:
- CGA参数为老年人死亡风险提供了可靠的预后信息.
- 功能状态下降和炎症标志物是比时间年龄更强大的死亡率预测因素.
- 机器学习模型,特别是人工神经网络,显示出提高老年病学死亡率预测准确性的巨大潜力.
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