开发基于机器学习的算法,用于远程监测和评估呼吸系统疾病
Negar Orangi-Fard1, Alexandru Bogdan2, Hersh Sagreiya3
1Department of Mathematics, Georgia Gwinnett College, Lawrenceville, GA 30043, United States.
Military medicine
|September 23, 2025
概括
机器学习准确地预测呼吸模式,使远程呼吸监测成为更好的患者护理. 这种人工智能驱动的方法对于及时干预至关重要,特别是在现场环境中.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 呼吸系统生理学 呼吸系统生理学
背景情况:
- 呼吸模式反映了呼吸的工作和呼吸系统的状况.
- 目前的监测依赖于临床试验,需要在现场环境中进行远程评估.
- 持续的正气道压力 (CPAP) 治疗需要监测.
研究的目的:
- 开发用于远程呼吸道疾病监测的机器学习算法.
- 评估接受CPAP治疗的患者.
- 在资源有限的环境中促进远程呼吸评估.
主要方法:
- 收集了30名健康成年人的呼吸压力,流量和胸腹部数据.
- 训练有素的机器学习模型 (随机森林,物流回归,SVM) 来分类呼吸条件 (正常,喘息,深呼吸).
- 在模型评估中使用了leave-one-out和k-fold交叉验证.
主要成果:
- 随机森林分类器在将呼吸速率作为一个特征时实现了88%的准确性.
- 由人工智能驱动的系统显示出远程呼吸评估的潜力.
- 这些发现支持从临床到远程环境的呼吸评估的过渡.
结论:
- 精确的呼吸模式检测对于识别临床恶化至关重要,特别是在部署的士兵中.
- 早期检测可以在资源有限的环境中改善结果,例如野战医院.
- 人工智能驱动的远程监控可以提高及时干预和患者护理.
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