在大型语言模型的创新中,通过对风险的批判性评估,在创新中强大的隐私
Yao-Shun Chuang1, Atiquer Rahman Sarkar2, Yu-Chun Hsu1
1McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.
概括
基于关键字的方法有效地产生私人,可用的合成临床笔记. 这种方法平衡了数据实用性和隐私,增强了全球生物医学研究数据共享.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 自然语言处理自然语言处理.
- 生物医学数据科学 生物医学数据科学
背景情况:
- 电子健康记录 (EHR) 对患者护理至关重要,但对研究提出隐私挑战.
- 大型语言模型 (LLM) 提供了生成合成健康数据的潜力.
- 在伦理研究中,平衡数据实用性和患者隐私是至关重要的.
研究的目的:
- 评估LLM在生成安全,符合健康保险可移植性和问责法案 (HIPAA) 的合成临床笔记方面.
- 评估合成笔记对生物医学研究的隐私和实用性.
- 为了比较不同的LLM生成方法用于合成数据创建.
主要方法:
- 在MIMIC III数据集上使用了GPT-3.5,GPT-4和Mistral 7B (去识别和重新识别).
- 使用模板和关键字提取用于笔记生成,与一次性生成进行比较.
- 通过受保护健康信息 (PHI) 的发生和共发生评估隐私.
- 通过培训ICD-9编码器和测量用ROUGE和共弦相似性测量文本质量的评估实用性.
主要成果:
- 基于关键字的方法显示隐私风险低,性能好.
- 一次性生成显示了更高的PHI暴露,特别是在位置和日期.
- 标准化的一次性方法实现了卓越的分类准确性.
- 重新识别的数据通常比非识别的数据产生更好的结果.
结论:
- 基于关键字的方法为生成保护隐私的合成临床笔记提供了可行的解决方案.
- 在数据实用性和隐私保护之间存在着关键的平衡.
- 使用LLM的合成数据生成可以改善用于研究的临床数据共享.
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