结合联合机器学习和定性方法来研究小儿喘新型亚型:混合方法研究协议
Jie Xu1, Sankalp Talankar1, Jinqian Pan1
1Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, United States.
JMIR research protocols
|July 8, 2024
概括
这项研究使用联合机器学习从现实数据中识别儿科喘亚型,为精确护理铺平道路. 保护隐私的方法使多个地点的协作成为可能,以更好地管理儿童的喘.
科学领域:
- 计算生物学是一种计算生物学.
- 在医疗保健中的数据科学.
- 儿科呼吸系统医学 儿科呼吸系统医学
背景情况:
- 儿童喘是一种复杂的疾病,具有有限的亚型表征.
- 机器学习 (ML),特别是深度神经网络,可以使用电子健康记录 (EHR) 来识别子类型.
- 联合学习 (FL) 用大型EHR数据集来解决隐私问题,用于多站点ML开发.
研究的目的:
- 在临床研究网络中开发一个用于联合ML实施的研究协议.
- 识别和描述儿科喘亚型及其随时间的进展.
- 调查已识别的亚型的临床实用性,以改善患者管理.
主要方法:
- 使用来自OneFlorida+临床研究网络 (2011-2023) 的数据.
- 开发一个儿科喘可计算的表型和NLP管道.
- 应用联合学习用于亚型发现和时间进展分析.
- 进行临床医生焦点小组和以用户为中心的设计,以实现EHR可视化.
主要成果:
- 收集了来自411,628名儿科患者 (年龄在2-18岁) 和11,156,148名临床笔记的数据.
- 预计将在1年完成可计算的表型化,并在2-3年完成亚型化.
- 专注组和以用户为中心的设计计划为4-5年.
结论:
- 从各种现实世界数据 (RWD) 获得的儿科喘亚型可以推进精确护理.
- 保护隐私的联合学习方法克服了多中心RWD分析中的挑战.
- 这种方法有助于将人工智能转化为临床实践,以改善儿科喘结果.
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