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使用电子健康记录预测跌倒:一个时间序列方法

Peter J Hoover¹, Terri L Blumke¹, Anna D Ware¹

¹National Center for Collaborative Healthcare Innovation, Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA 94304, United States.

|October 6, 2025

概括

这项研究为退伍军人卫生管理局患者开发了一种先进的秋季预测模型,其性能优于传统方法. 新模型使用电子健康记录来更准确,更有效地识别落风险.

科学领域:

医疗保健信息学医疗保健信息学
机器学习在医学中的应用
患者安全患者安全

背景情况:

传统的跌倒风险评估工具,如摩尔斯跌倒量表 (MFS),在准确性方面存在局限性.
准确的跌倒预测对于急性护理机构的患者安全至关重要.
电子健康记录 (EHR) 包含丰富的数据,用于开发预测模型.

研究的目的:

为退伍军人卫生管理局 (VHA) 内的患者开发和验证更准确的跌倒预测模型.
为了利用现有的EHR数据进行自动化和高效的跌倒风险评估.
提高现有的落风险评估工具的预测能力.

主要方法:

分析了2020年7月至2022年6月期间入住VHA急症监护机构的退伍军人队列.
从电子健康记录中提取了人口统计和临床数据,通过临床进展笔记识别了下降.
使用变压器模型进行特征提取,然后训练光梯度增强机进行分类.

主要成果:

该研究分析了242,844名退伍军人,其中2.5%的人经历了记录下来的跌倒.
开发的模型实现了0.851的曲线下的面积 (AUC) 和0.285.285的精度召回曲线下的面积 (AUPRC).
该模型的准确度为76.3%,特异性为76.2%,灵敏度为77.3%.

结论:

开发的机器学习模型显著优于传统方法,如摩尔斯跌倒量表用于跌倒风险预测.
这种自动化模型是从现有的EHR数据中得出的,它提供了一种更有效,更准确的方法来识别有风险的患者.
需要进一步的研究来解决阶级不平衡问题,并进行前性验证以实现更广泛的实施.

关键词:

退伍军人退伍军人退伍军人电子健康记录是电子医疗记录.秋季预测秋季预测跌倒风险评估跌倒风险评估时间序列分类时间序列分类

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