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机器学习技术用于诊断阻塞性睡眠呼吸暂停/呼吸暂停综合征的应用

Oscar Bedoya¹, Santiago Rodríguez¹, Jenny Patricia Muñoz²

¹School of Systems Engineering and Computer Science, Universidad del Valle, Cali 760032, Colombia.

Life (Basel, Switzerland)

|May 25, 2024

概括

机器学习模型,特别是随机森林和额外树木,为诊断阻塞性睡眠呼吸暂停/呼吸暂停综合征 (OSAHS) 提供了一种高效且易于使用的方法. 这些技术显示出高精度,改进了传统的多睡眠学 (PSG).

科学领域:

睡眠医学睡眠医学
人工智能的人工智能
心血管健康心血管健康

背景情况:

阻塞性睡眠呼吸暂停/睡眠暂停综合征 (OSAHS) 具有显著的心血管和神经心理风险.
在睡眠期间反复出现的上呼吸道阻塞会导致低氧化和小排气.
多睡眠图 (PSG) 是OSAHS诊断的黄金标准,但成本昂贵且难以获得.

研究的目的:

开发和评估用于可访问和高效的OSAHS诊断的机器学习模型.
为了比较各种机器学习算法的性能,用于OSAHS选.
为医疗从业者创建一个实用的工具,以帮助OSAHS诊断.

主要方法:

利用了机器学习技术,包括神经网络,决策树,随机森林和额外树.
在哥伦比亚睡眠中心 (2014-2016) 的601名成年患者数据集上训练并验证了模型.
开发了一个集成随机森林模型的Web应用程序,用于临床使用.

主要成果:

组合方法,随机森林和额外树木,证明了高的诊断效率.
在ROC曲线下的面积为随机森林的89.2%,额外树木的89.6%.
该网络应用程序使用18个输入变量来促进OSAHS选.

结论:

机器学习,特别是组合方法,为OSAHS诊断提供了有效的替代方案.
开发的Web应用程序可以支持医疗专业人员及时进行OSAHS查.
这种方法提高了OSAHS诊断程序的可访问性和效率.

关键词:

人工智能的人工智能是人工智能.整体方法整体方法机器学习是机器学习.神经网络的神经网络的神经网络阻塞性睡眠呼吸暂停 (SOP) 的情况.

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