通过连接的移动设备进行家庭监控,以通过机器学习来预测喘发作
Kevin C H Tsang1,2, Hilary Pinnock3, Andrew M Wilson3,4,5
1Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK. kevin.tsang@ed.ac.uk.
Scientific data
|June 8, 2023
概括
这项研究引入了用于喘自我管理的新数据集. 它使用智能设备进行被动监测,旨在改善喘发作预测和减少患者负担.
科学领域:
- 数字健康数字健康
- 呼吸系统药物 呼吸系统药物
- 机器学习 机器学习
背景情况:
- 喘自我管理至关重要,但往往是繁重的.
- 传统的监测需要积极的患者参与,这可能是繁的.
- 现有的数据集缺乏客观的,被动收集的数据用于机器学习.
研究的目的:
- 为了解决喘监测算法数据的稀缺性.
- 探索被动监测对于喘自我管理的可行性.
- 为了收集丰富的数据集来预测喘发作.
主要方法:
- 在英国进行了一项为期7个月的观察性研究 (AAMOS-00) 与22名参与者.
- 使用智能设备 (峰值流量计,吸入器,智能手表) 和每日症状问卷.
- 综合环境数据 (天气,花粉,空气质量) 与被动收集的数据.
主要成果:
- 在2021年6月至2022年6月期间收集了2,054个患者日的数据.
- 创建了一个丰富的,匿名的纵向数据集,将设备,症状和环境数据结合起来.
- 证明了被动监测喘的可行性.
结论:
- 这项研究为喘研究提供了有价值的,公开可用的数据集.
- 通过智能设备进行被动监测,有望减少喘管理负担.
- 该数据集可以帮助开发用于预测喘发作的机器学习模型.
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