细粒度患者相似度测量使用对比图相似度网络
Yuxi Liu1, Zhenhao Zhang2, Shaowen Qin1
1College of Science and Engineering, Flinders University, Adelaide, SA, Australia.
概括
这项研究引入了一种新的对比图相似性网络,以改善从电子健康记录 (EHR) 中学习患者代表性. 该方法增强了相似性计算,以获得更好的临床预测,例如生命体征归因.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于深度学习的进步,电子健康记录 (EHR) 越来越多地用于预测分析.
- 从电子健康记录中学习患者的代表性是一个关键领域,但现有的方法在不规则的数据和患者相似性方面扎.
- 当前的深度学习模型经常忽视患者的相似性,这是临床推理的关键方面.
研究的目的:
- 开发一种新的方法来计算大型电子病历数据集中的患者相似性.
- 通过结合相似信息来生成丰富的患者表示.
- 通过使用增强的患者表示来改进下游预测任务.
主要方法:
- 为了计算患者的相似性,开发了一个对比图相似性网络.
- 基于图形的相似性分析被用来提取临床特征.
- 来自相似患者的信息被汇总在一起,以创建强大的患者表示.
主要成果:
- 拟议的方法在患者之间的相似性计算方面表现出有效性.
- 实验结果显示,在现实世界EHR数据上的实验结果优于现有方法.
- 这种方法提高了生命体征归算和ICU患者恶化预测的性能.
结论:
- 对比图形相似性网络有效地解决了当前EHR患者代表性学习的局限性.
- 纳入患者相似性显著提高预测模型的性能.
- 这种方法为使用EHR数据的临床决策支持提供了一个有希望的方法.
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