为了识别新的风险厌恶和随后的限制和偏见,当在后LLM世界中公开提供非识别结构化数据集时
Fangyi Chen1, Kenrick Cato2,3, Gamze Gürsoy1
1Department of Biomedical Informatics, Columbia University, New York, NY, United States.
概括
本研究详细介绍了临床数据的非识别方法,解决了大型语言模型 (LLM) 时代的重新识别风险,以提高开放科学和研究透明度.
科学领域:
- 临床数据科学 临床数据科学
- 医疗信息学 医疗信息学
- 研究 透明度 研究透明度
背景情况:
- 开放的临床数据集对于可重复的科学研究至关重要.
- 目前,公共临床数据集的可用性有限.
- 该研究旨在通过发布其结构化临床数据,为开放科学做出贡献.
研究的目的:
- 为结构化临床数据提供非识别方法.
- 考虑在未来包括非识别的叙事笔记.
- 在大型语言模型 (LLM) 时代解决重新识别风险.
主要方法:
- 文献审查,以告知去识别策略.
- 关于数据集发布的决策的协作共识会议.
- 评估每个非识别选择的优缺点.
主要成果:
- 在发布结构化临床数据集方面做出了知情决定.
- 概述了被非识别算法引入的限制和偏见.
- 这项工作是第一个专门为LLM时代描述非识别理性的工作.
结论:
- 对公开可用的数据集来说,透明地披露去识别决策至关重要.
- 了解局限性和偏见对于非识别数据的用户来说至关重要.
- 这项研究为消除临床数据的识别提供了一个框架,同时考虑未来的风险.
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