在使用无监督表示学习的顺序电子医疗数据中发现患者组
Jingteng Li1,2, Kimberley R Zakka1,2, John Booth1,2
1Great Ormond Street Institute of Child Health, University College London, London, UK.
BMC medical informatics and decision making
|January 28, 2025
概括
来自电子健康记录 (EHR) 的无监督学习创建了患者特定的功能. 这些特征揭示了儿科重症监护中的患者个人资料和治疗方法的显著差异,证明了临床实用性.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
背景情况:
- 自然语言处理 (NLP) 启发的无监督特征学习方法可以从纵向电子健康记录 (EHR) 中获得患者特征.
- 这些方法为分析复杂的患者数据提供了一种新的方法.
研究的目的:
- 将文档嵌入算法应用于儿科重症监护室 (PICU) EHR数据.
- 从1853名患者的ICU行程中提取患者特定的特征.
- 通过K-means集群评估这些特征的临床实用性.
主要方法:
- 文档嵌入算法应用于包括实验室测试和药物事件在内的EHR数据.
- 训练了一种文档嵌入模型,并用于生成潜在的患者特征向量.
- 在患者载体上进行了K-means聚类以进行下游分析.
主要成果:
- 在所有1853名患者中成功生成了潜在患者特征向量.
- 无监督集群确定了5个不同的患者集群.
- 跨集群观察到患者特征,手术干预和诊断资料的显著变化 (p<0.0001).
结论:
- K-means集群证实了从嵌入算法中获得的患者特征的临床实用性.
- 学习的潜在患者特征直接适用于其他机器学习算法.
- 未来的研究将纳入时间EHR信息,并扩展嵌入算法,用于个性化患者旅程预测.
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