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检查站专家组合使用可穿戴设备进行可解释性发作检测

Joe Germino^1,2, Benjamin Brinkmann³, Nitesh V Chawla²

¹Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.

Scientific reports

|December 17, 2025

概括

一个新的检查点专家混合 (MoCE) 算法可以通过可穿戴设备改善发作检测. 这种透明的机器学习模型比传统的监测黑盒方法提供了更好的性能和可解释性.

科学领域:

生物医学工程生物医学工程
人工智能在医学中的应用
神经学神经学

背景情况:

医院视频脑电图 (vEEG) 是发作检测的标准,但资源密集.
可穿戴设备为长期在家监测发作提供了一个可行的替代方案.
现有的机器学习 (ML) 模型经常充当黑子,阻碍临床信任和可审计性.

研究的目的:

引入一种新的,透明的ML算法,检查点专家混合 (MoCE),用于发作检测.
与传统的黑子模型相比,评估MoCE的性能和可解释性.
为了证明透明ML在管理中的临床实用性.

主要方法:

检查点专家混合 (MoCE) ML算法的开发.
在14名患者中部署手腕戴式设备进行数据收集.
对MoCE与现有神经网络进行比较分析,以确定发作检测准确率和错误报警率.

主要成果:

与现有的神经网络相比,MoCE在虚假报警率上表现出了统计学上显著的改善.
MoCE实现了同等的召回性能,同时提供了增强的模型透明度.
该算法为模型行为提供了全球和本地洞察力,有助于预测审计.

结论:

对于发作检测,MoCE提供了一个透明和有效的替代黑盒ML模型.
该算法的可解释性提高了临床医生和数据科学家的信任和实体监测中的实用性.
MoCE在利用可穿戴技术和ML用于治疗方面取得了重大进展.

关键词:

可解释的机器学习专家们的混合研究.扣押检测检测扣押检测扣押检测可穿戴设备是可以穿戴的.

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