从时间序列中学习和解患者的静态信息 电子健康记录 (STEER)
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLOS digital health
|October 21, 2024
概括
机器学习模型可以从电子健康记录中预测敏感的患者信息,如种族和性别. 研究人员开发了一种新方法来保护这些数据在医疗保健AI.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 医疗保健中的机器学习引发了患者隐私和算法公平性的担忧.
- 自我报告的种族可以从缺乏明确种族信息的医疗数据中预测,但范围是未知的.
- 开发受敏感属性影响最小的模型具有挑战性.
研究的目的:
- 系统地调查时间序列电子健康记录 (EHR) 数据对患者静态信息的预测能力.
- 评估从ML模型中原始和学习的表示能够编码敏感的患者属性的能力.
- 开发使用EHR数据的机器学习模型的隐私保护方法.
主要方法:
- 系统地调查时间序列的EHR数据,以预测静态患者信息.
- 在原始和学习的表示上训练机器学习模型,以预测生物性别,年龄和自我报告的种族.
- 开发一个变化自编码器 (VAE) 方法来解开敏感属性.
主要成果:
- 机器学习模型从EHR数据中实现了生物性别 (AUC 0.851),二元化年龄 (AUC 0.869) 和自我报告的种族 (AUC 0.810) 的高预测性能.
- 在不同的任务,队列,模型架构和数据库中,高预测性能是一致的.
- 基于VAE的方法成功地学习了潜在空间,以从时间序列数据中解脱敏感的患者属性.
结论:
- 时间序列 EHR 数据和学习表示包含了重要的患者敏感信息.
- 现有的机器学习模型可以无意中编码和预测敏感属性.
- 一种基于VAE的新方法提供了一种可通用的方法来保护医疗保健AI中的患者隐私.
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