快速EHR:有条件的电子医疗记录生成与快速学习
1University of Illinois Urbana-Champaign.
Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing
|February 14, 2025
概括
产生现实的合成电子医疗记录 (EHR) 对机器学习应用至关重要. 本研究引入了一种新的文本到文本翻译方法,使用语言模型来灵活和保护隐私的电子健康记录生成.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 由于隐私问题,访问纵向多式联络电子医疗记录 (EHR) 受到阻碍,限制了医疗保健中的机器学习 (ML) 应用.
- 现有的合成EHR生成方法缺乏灵活性,并产生不切实际的数据,通常生成单模记录或依赖于有限的纵向推断.
研究的目的:
- 开发一种灵活且保护隐私的方法,用于生成现实的多式联机合成电子健康资料.
- 通过利用文本到文本翻译的语言模型来解决当前EHR生成技术的局限性.
主要方法:
- 通过语言模型 (LMs) 将EHR生成表达为文本对文本翻译任务.
- 实施基于人口特征的受控生成的快速学习.
- 使用纵向归算困难度 (lpl) 和交叉模式归算困难度 (mpl) 评估合成EHR质量.
- 使用会员身份和属性推断攻击进行隐私保护评估.
主要成果:
- 提出的基于LM的文本对文本翻译方法显著提高了合成电子健康记录的现实性.
- 与基线方法相比,已证明优越性,lpl的平均下降率为53.1%,mpl的平均下降率为45.3%.
- 该方法确保隐私风险较低,这是隐私攻击评估验证的.
结论:
- 基于语言模型的文本对文本翻译为合成EHR生成提供了高度灵活和有效的方法.
- 这种方法克服了现有技术的局限性,产生了更现实的,保护隐私的纵向多式联络电子健康记录.
- 这些发现为在医疗保健中更广泛地采用ML铺平了道路,因为它可以安全地利用EHR数据.
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