DIRI：利用大型语言模型进行对抗性患者再识别以评估临床文本匿名化
DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization
AI总结：
本文提出DIRI方法，利用大型语言模型对经去标识化的临床记录进行对抗性患者再识别，发现即使最佳工具ClinicalBERT处理后仍有9%的记录可被再识别，揭示了现有匿名化技术的不足。
摘要：
共享受保护的健康信息（PHI）对于推进生物医学研究至关重要。在数据分发之前，从业者通常执行去标识化以移除文本中包含的任何PHI。当代去标识化方法在高度饱和的数据集上进行评估（工具达到近乎完美的准确率），这可能无法反映真实世界临床文本的全部变异性或复杂性，并且对其进行标注是资源密集型的，这构成了真实世界应用的障碍。为弥补这一差距，我们开发了一种对抗性方法，使用大型语言模型（LLM）来再识别与经过编辑的临床记录相对应的患者，并通过一种新颖的去标识化/再标识化（DIRI）方法评估其性能。我们的方法使用大型语言模型来再识别与经过编辑的临床记录相对应的患者。我们在来自威尔康奈尔医学院的医疗数据上展示了我们的方法，这些数据使用三种去标识化工具进行了匿名化：基于规则的Philter以及两种基于深度学习的模型BiLSTM-CRF和ClinicalBERT。尽管ClinicalBERT最为有效，掩盖了所有识别出的PII，但我们的工具仍然再识别出了9%的临床记录。我们的研究突出了当前去标识化技术的重大弱点，同时为迭代开发和改进提供了一个工具。
英文摘要：
Sharing protected health information (PHI) is critical for furthering biomedical research. Before data can be distributed, practitioners often perform deidentification to remove any PHI contained in the text. Contemporary deidentification methods are evaluated on highly saturated datasets (tools achieve near-perfect accuracy) which may not reflect the full variability or complexity of real-world clinical text and annotating them is resource intensive, which is a barrier to real-world applications. To address this gap, we developed an adversarial approach using a large language model (LLM) to re-identify the patient corresponding to a redacted clinical note and evaluated the performance with a novel De-Identification/Re-Identification (DIRI) method. Our method uses a large language model to reidentify the patient corresponding to a redacted clinical note. We demonstrate our method on medical data from Weill Cornell Medicine anonymized with three deidentification tools: rule-based Philter and two deep-learning-based models, BiLSTM-CRF and ClinicalBERT. Although ClinicalBERT was the most effective, masking all identified PII, our tool still reidentified 9% of clinical notes Our study highlights significant weaknesses in current deidentification technologies while providing a tool for iterative development and improvement.
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