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机器学习与双项逻辑回归分析对基于SPPB得分的老年成人门诊患者的跌倒风险
Sho Hasegawa1,2, Fumihiro Mizokami2,3, Yoshitaka Kameya4
1Department of Pharmacotherapeutics and Informatics, Fujita Health University, Toyoake, Japan.
Digital health
|December 18, 2023
概括
一种使用商业机器学习软件 (MLS) 预测老年人跌倒风险的新诊断模型表现出与传统的二项逻辑回归分析 (BLRA) 相当的性能. 这一发现表明,MLS是评估跌倒风险的可行工具.
科学领域:
- 老年学是一门学科.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 对老年人来说,跌倒风险是一个重要的关注点,导致受伤和生活质量下降.
- 准确预测跌倒风险对于实施及时的预防性干预至关重要.
- 短物理性能电池 (SPPB) 是评估身体功能和跌倒风险的广泛使用的工具.
研究的目的:
- 使用商业机器学习软件 (MLS) 开发的跌倒风险预测模型的诊断性能与二项逻辑回归分析 (BLRA) 进行比较.
- 根据SPPB得分,评估MLS在识别患有倒风险的老年成人门诊患者方面的有效性.
主要方法:
- 分析了797名门诊患者的队列,数据分为开发和验证集.
- 用SPPB得分 (男性≤6,女性≤9) 定义了跌倒风险.
- 模型使用包括曲线下面面积 (AUC),准确性,精度,回忆,特异性和F度等指标进行评估.
主要成果:
- 该MLS模型实现了0.78的AUC,0.74的准确性,0.81的回忆和0.71.71的特异性.
- BLRA模型的AUC达到0.77,准确度为0.75,回忆率为0.67,特异性为0.77.
- 通过MLS识别的关键预测因素包括手握强度,女性性别,跌倒史,BMI和小腿周长.
结论:
- 使用商业MLS开发的跌倒风险诊断模型与使用BLRA开发的模型相比并非劣.
- 商业MLS为在临床环境中构建跌倒风险诊断模型提供了一种可行和有效的方法.
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