从自我报告事件和心率的周期预测发作概率:一项前性试点研究
Wenjuan Xiong1, Rachel E Stirling2, Daniel E Payne3
1School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, Australia.
EBioMedicine
|June 18, 2023
概括
使用多式联网可穿戴数据预测发作风险显示出有希望. 在可扩展的算法中将和心率周期结合起来,可以提高预测准确度,这是临床应用的关键步骤.
科学领域:
- 生物医学工程 生物医学工程
- 的研究研究.
- 可穿戴技术可穿戴技术
背景情况:
- 预测发作风险可以预防伤害和死亡.
- 可穿戴设备提供了一种非侵入性方法,用于生成发作风险预测.
- 以前的研究表明,使用活动,发作时间或心率周期有前途.
研究的目的:
- 为了验证一种使用可穿戴设备的多式联络周期来预测发作风险的方法.
- 评估联合发作和心率周期预测算法的性能.
- 评估可扩展发作风险预测模型的临床适用性.
主要方法:
- 从使用智能手机应用程序和智能手表的13名参与者中提取了和心率周期.
- 研究了发作和抽取周期的阶段之间的关系.
- 采用添加回归模型来预测心率周期,并比较预测性能.
- 使用长期数据对6名参与者进行了前性验证.
主要成果:
- 追溯验证显示,在9/13名参与者中,随机以上的平均AUC为0.73.
- 前性评估的平均AUC为0.77,其中4/6名参与者表现高于机会.
- 综合多式联运周期预测算法表现出强大的性能.
结论:
- 多模式数据周期可以集成到可扩展的风险预测算法中,以获得强大的性能.
- 预测方法允许对任意未来时期进行扣押风险估计,并且是可概括的.
- 使用失明受试者的前性评估代表了临床风险预测应用的关键进步.
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