使用机器学习对事件轨迹进行集群:电子医疗记录的方法
1Institute of Health Sciences, University of Leeds, UK.
Studies in health technology and informatics
|May 17, 2025
概括
人工智能 (AI) 可以分析患者的健康轨迹,以确定风险. 一种新的人工智能方法,TG-CNN,通过考虑事件之间的时间,有效地将多个长期条件 (MLTC) 集成在一起.
科学领域:
- 计算医学是一种计算医学.
- 医疗信息学 医疗信息学
- 机器学习用于医疗保健
背景情况:
- 由于人口老龄化,多重疾病,定义为多重长期疾病 (MLTCs),正在增加.
- 对MLTCs的分散护理增加了不良药物相互作用和药物疾病相互作用的风险.
- 识别患有MLTC的高风险患者,特别是脆弱个体,是具有挑战性的.
研究的目的:
- 适应和评估机器学习方法,以集群多种长期疾病 (MLTC) 的患者轨迹.
- 解决现有方法的局限性,这些方法忽视了电子健康记录 (EHR) 中临床事件的时间方面.
主要方法:
- 适应了三个机器学习算法:Word2Vec,Autoencoder和时间图卷积神经网络 (TG-CNN).
- 使用了ACT-MOOC数据集,使用用户交互时间表作为EHR数据的代理.
- 专注于对整个患者/用户轨迹进行聚类,并纳入事件之间的时间.
主要成果:
- 时间图卷积神经网络 (TG-CNN) 在集群轨迹方面表现出卓越的表现.
- TG-CNN在不同集群之间实现了明确的分离,有效地代表了患者/用户旅程.
- 提出的方法,特别是TG-CNN,显示出分析复杂健康轨迹的前景.
结论:
- 考虑到事件间的时间,TG-CNN方法在聚类多病症患者轨迹方面取得了重大进展.
- 这种方法在现实世界医疗保健环境中具有潜在的应用,例如CPRD,以改善患者风险分层.
- 未来的研究将在NIHR资助的DynAIRx项目中使用临床数据验证这种方法.
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