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使用机器学习预测医院内跌倒风险,使用实时定位系统和电子医疗记录的机器学习
Dong Won Kim1, Jihoon Seo1, Sujin Kwon2
1Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.
Journal of cachexia, sarcopenia and muscle
|February 25, 2025
概括
实时定位系统 (RTLS) 与临床数据相结合,显著改善了医院倒的预测. 使用这两种数据类型的机器学习模型通过早期检测跌倒风险来提高患者的安全性.
科学领域:
- 医疗保健技术 医疗保健技术 医疗保健技术
- 患者安全 患者安全
- 机器学习在医学中的应用
背景情况:
- 医院摔倒是导致患者伤害和护理质量下降的主要原因.
- 实时定位系统 (RTLS) 提供持续的患者监测,以跟踪身体活动,这是一个关键的跌倒风险指标.
研究的目的:
- 利用RTLS数据进行动态患者运动分析.
- 用机器学习将运动数据与临床信息集成在一起,以改善医院内跌倒预测.
主要方法:
- 追溯性研究比较了三个模型:仅临床数据,仅RTLS数据和组合数据.
- 来自永分娩医院的561名患者 (118名患者,443名患者) 的分析 (2020年3月至2022年6月).
- 性能使用AUROC,AUPRC,Brier评分进行评估;可解释性使用SHAP和DCA进行评估.
主要成果:
- 结合的临床和RTLS模型实现了最高的预测准确性 (AUROC:0.847).
- RTLS数据显示,在跌倒之前活动减少和运动放缓.
- 镇静剂的使用和更高的红细胞分布宽度 (RDW) 与跌倒发生率的增加有关.
结论:
- RTLS有效地跟踪身体活动变化,用于医院内跌倒预测.
- 将RTLS与机器学习集成,可以提升早期降落风险检测.
- 这种方法有望提高患者的安全性并减少跌倒事件.
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