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SelANet:基于信心评分的选择性睡眠呼吸暂停检测,以协助决策

Beomjun Bark¹, Borum Nam², In Young Kim³

¹Department of Biomedical Engineering, Hanyang University, 222, Wangsimni-Ro, Seongdong-Gu, 04763, Seoul, Republic of Korea.

BMC medical informatics and decision making

|September 22, 2023

概括

这项研究引入了一种选择性预测算法,用于使用有限的生物信号检测睡眠呼吸暂停综合征. 人工智能模型通过对高可靠性样本进行分类,并为临床医生标记不确定样本,从而提高了传统方法的准确性.

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
睡眠医学睡眠医学

背景情况:

睡眠呼吸暂停综合征是一种常见的疾病,通过多睡眠学诊断,这是劳动密集且昂贵的.
使用有限的生物信号的自动检测算法正在开发中,以克服多睡眠学的局限性.
由于有限的数据,人工智能判断的不确定性需要改进诊断方法.

研究的目的:

开发一种自动化睡眠呼吸暂停检测算法,使用基于信心评分的选择性预测.
为了解决AI驱动的诊断中的不确定性问题,从有限的生物信号.
提供一个临床医生支持工具,以确定需要多睡眠学.

主要方法:

利用了994名受试者的多睡眠学数据.
使用自动编码器从潜伏向量中采用特征提取.
设计和训练了一种1D卷积神经网络-长期短期记忆 (1D CNN-LSTM) 模型,其基于信心分数的选择性预测功能.

主要成果:

开发的模型在分类睡眠呼吸暂停时实现了90.26%的准确性,91.29%的灵敏性和89.21%的特异性.
与没有该功能的模型相比,选择性预测将所有性能指标提高了约7.03%.
该算法证明了经验覆盖与目标覆盖的趋同,覆盖违规率最小 (0.067).

结论:

选择性预测算法有效地减少了由于有限的生物数据而产生的诊断不确定性.
这种方法适用于可穿戴设备,可以作为简单的查工具或补充诊断方法.
该算法提高了睡眠呼吸暂停诊断的效率和可访问性.

关键词:

人工智能的人工智能是人工智能.关于提供援助的决定.有选择性的预测预测.睡眠呼吸暂停综合征是什么可穿戴设备是可以穿戴的.

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