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强化机器学习方法用于OSA患者查:模型开发和验证研究.

Rongrong Dai^1,2, Kang Yang^3,4, Jiajing Zhuang³

¹The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.

Scientific reports

|August 26, 2024

概括

使用年龄,性别,BMI和心率的机器学习模型可以预测阻塞性睡眠呼吸暂停 (OSA). 人工神经网络模型在早期OSA诊断和临床决策方面表现最好.

关键词:

机器学习是机器学习.移动睡眠药物管理平台移动睡眠药物管理平台阻塞性睡眠呼吸暂停症是什么多重睡眠学术 (Polysomnography) 是一种多重睡眠学术.预测模型的预测模型.

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科学领域:

睡眠医学睡眠医学
医疗保健中的人工智能
生物医学信息学生物医学信息学

背景情况:

阻塞性睡眠呼吸暂停 (OSA) 是一种普遍的疾病,具有可识别的风险因素,包括年龄,性别,体重指数 (BMI) 和睡眠期间的平均心率.
准确预测OSA对于及时干预和管理至关重要,但当前的方法可能很复杂或无法获得.

研究的目的:

开发和评估机器学习模型,以使用简单,可访问的参数来预测中度至严重的OSA.
将这些预测模型集成到基于云的移动平台中,以提高临床效用.

主要方法:

利用了610名接受多睡眠图 (PSG) 的患者的临床数据.
应用后勤回归,人工神经网络,天真贝叶斯,支持矢量机,随机森林和决策树算法.
经过训练和验证的模型使用年龄,性别,BMI和睡眠期间的平均心率作为预测因素.

主要成果:

所有六种机器学习模型都有效地预测了中度至重度的OSA.
人工神经网络模型实现了最高的性能,AUROC为80.4%,准确率为69.9%.
其他车型也表现出了竞争力的表现,AUROCs在70.4%至80.2%之间.

结论:

年龄,性别,BMI和心率等简单参数是OSA的重要预测因素.
机器学习模型,特别是人工神经网络,为早期OSA诊断提供了可靠的工具.
集成到移动平台可以改善睡眠医学的临床决策.