使用BEHRT嵌入医疗概念的联合学习
Ofir Ben Shoham1, Nadav Rappoport1
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
JAMIA open
|October 24, 2024
概括
通过BEHRT模型的联合学习 (FL),可以有效地预测下一个患者使用电子健康记录 (EHR) 访问,而无需集中敏感数据. 这种方法与集中式模型的性能密切相匹配,并且明显优于本地模型.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 医疗保健数据分析数据分析
背景情况:
- 电子健康记录 (EHR) 数据是敏感的,阻碍了多中心数据共享,以开发可靠的预测模型.
- 联合学习 (FL) 能够在多个地点进行协作模式培训,而无需集中数据.
研究的目的:
- 通过使用BEHRT模型对EHR数据进行下一次访问预测来评估联合学习 (FL) 方法.
- 对EHR预测任务的集中和本地模型相比,评估FL的性能.
主要方法:
- 提出了一种FL方法,使用BEHRT模型学习医疗概念的嵌入.
- 在MIMIC-IV数据集上使用FL训练了面具语言建模 (MLM) 和下一次访问预测模型.
主要成果:
- FL方法实现了与集中式模型可比的性能,平均精度差异为0% - 3%.
- 飞机模型显示出相对于本地模型的显著改进,平均精度提高了4%-10%.
- 预先训练的MLM提高了下一次访问诊断预测任务的性能.
结论:
- 联合学习 (FL) 是一种可行的策略,用于从分布式EHR数据中开发强大的预测模型.
- 拟议的FL方法,利用BEHRT和预训练的MLM,有效地解决了数据隐私问题,同时实现了高预测准确度.
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