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大型语言模型授权的隐私保护框架用于PHI在临床笔记中的注释.

Guanchen Wu1, Linzhi Zheng2, Han Xie1

  • 1Department of Computer Science, Emory University.

Studies in health technology and informatics
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概括
此摘要是机器生成的。

本研究介绍了LPPA,这是一个LLM驱动的框架,用于隐私保护健康信息 (PHI) 的注释. 它使用合成数据来提高准确性,同时保护患者的保密性.

关键词:
在法学士 (LLM) 课程中.这就是PHI PHI.在PHI注释中.临床注意事项 临床注意事项取消识别 取消识别

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科学领域:

  • 医疗信息学 医疗信息学
  • 自然语言处理自然语言处理.
  • 数据 隐私 数据 隐私 数据

背景情况:

  • 不识别私人健康信息对于保密性至关重要.
  • 现有的方法,如基于规则和基于学习的方法,缺乏通用性,需要大量的注释数据.
  • 大型语言模型 (LLM) 提供先进的语言理解,但存在隐私风险和高计算需求.

研究的目的:

  • 提出LPPA (LLM驱动的隐私保护的PHI注释),这是一个有效和私有健康信息注释的新框架.
  • 利用预先训练有素的LLM来利用少量射击学习来生成合成的临床笔记,最大限度地减少对大型注释数据集的需求.
  • 通过对LLMs进行本地微调来增强隐私保护和PHI注释准确性.

主要方法:

  • 使用预先训练的LLMs进行短暂的学习,以生成合成的临床笔记.
  • 在生成的合成数据上实施LLM的本地微调.
  • 开发LPPA框架用于隐私保护的PHI注释.

主要成果:

  • 在PHI注释准确度方面已证明有效性.
  • 在注释过程中实现了高效率和可扩展性.
  • 成功生成合成临床笔记,以减少数据需求.

结论:

  • LPPA为隐私保护的PHI注释提供了一个强大的解决方案.
  • 该框架有效地平衡了隐私,准确性和效率.
  • LPPA显示出对非识别医疗记录的重大承诺.