[论文解读] DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

Zhengliang Liu, Huang, Yue+16 more|arXiv (Cornell University)|Mar 20, 2023

Topic Modeling被引用 89

一句话总结

本文提出 DeID-GPT，一种基于 GPT-4/ChatGPT 的零样本框架，通过将 HIPAA 标识符嵌入提示词来实现对自由文本医疗记录的自动去识别，并在处理笔记时对 PHI 进行涂改，同时尽量保持文本含义。其在 i2b2/UTHealth 去标识数据集上进行评估，并强调提示词设计是性能的关键驱动因素。

ABSTRACT

The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability and Accountability Act) mandates removing re-identifying information before the dissemination of medical records. Thus, effective and efficient solutions for de-identifying medical data, especially those in free-text forms, are highly needed. While various computer-assisted de-identification methods, including both rule-based and learning-based, have been developed and used in prior practice, such solutions still lack generalizability or need to be fine-tuned according to different scenarios, significantly imposing restrictions in wider use. The advancement of large language models (LLM), such as ChatGPT and GPT-4, have shown great potential in processing text data in the medical domain with zero-shot in-context learning, especially in the task of privacy protection, as these models can identify confidential information by their powerful named entity recognition (NER) capability. In this work, we developed a novel GPT4-enabled de-identification framework (``DeID-GPT") to automatically identify and remove the identifying information. Compared to existing commonly used medical text data de-identification methods, our developed DeID-GPT showed the highest accuracy and remarkable reliability in masking private information from the unstructured medical text while preserving the original structure and meaning of the text. This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text data processing and de-identification, which provides insights for further research and solution development on the use of LLMs such as ChatGPT/GPT-4 in healthcare. Codes and benchmarking data information are available at https://github.com/yhydhx/ChatGPT-API.

研究动机与目标

• 通过移除识别信息来推动自由文本医疗数据的符合 HIPAA 要求的共享。
• 利用 GPT-4/ChatGPT 的零样本上下文学习，在不进行微调的情况下识别并涂改临床笔记中的 PHI。
• 设计高质量的提示，将 HIPAA 标识符整合到其中以引导去标识，同时保留文本含义。

提出的方法

• 在零样本框架中使用 GPT-4/ChatGPT，通过嵌入 HIPAA 标识符的提示来指定去标识任务。
• 通过提示处理原始临床笔记以生成去标识输出。
• 在以提示驱动的工作流中，按照 HIPAA 类别对 PHI 进行映射和涂改。
• 使用合成替代物在 2014 年的 i2b2/UTHealth 去标识数据集上评估性能。
• 提供用于可重复性的开源代码和基准数据。

实验结果

研究问题

• RQ1 GPT-4/ChatGPT 能否在不进行微调的情况下实现对自由文本医疗笔记的高准确性去标识？
• RQ2 提示设计和 HIPAA 标识符集成如何影响去标识的性能？
• RQ3 零样本去标识在保留临床笔记原意和结构方面的效果如何？

主要发现

• DeID-GPT 在从非结构化临床文本中屏蔽私密信息方面达到对比方法中的最高准确性。
• 该方法在涂改 PHI 的同时保持了笔记的原始结构和含义。
• 提示工程，特别是将 HIPAA 标识符融入提示中，是性能的主要驱动因素。
• 该研究是将 ChatGPT/GPT-4 应用于医学文本处理和去标识的早期研究之一。
• 代码和基准数据公开可获取，便于复现。