用图结构保护精神科病历关键信息，同时降低身份泄露风险。
Anonpsy: A Graph-Based Framework for Structure-Preserving De-identification of Psychiatric Narratives
- 将病历转为包含实体与关系的语义图，实现结构化去标识
- 在保持临床结构前提下扰动识别信息，降低重识别风险
- 适合医疗数据隐私保护研究者与临床数据共享场景
精神科病历不仅通过显式标识符暴露患者身份，还通过独特的生命事件及其临床结构隐含身份信息。现有去标识方法（如隐私信息掩码和大模型重写）仅在文本层面操作，对保留或修改哪些语义元素控制不足。本文提出Anonpsy，一种基于图结构的去标识框架，将任务重构为图引导的语义重写：(1) 将每篇病历转换为包含临床实体、时间锚点及类型化关系的语义图；(2) 施加图约束扰动，在保留临床关键结构的同时修改识别性上下文；(3) 通过图条件化的大模型生成新文本。在90篇临床医生撰写的病例上评估，Anonpsy在专家、语义和GPT-5评估下均保持低重识别风险，且相比强基线大模型仅重写方案，显著降低语义相似度与可识别性。结果表明，显式结构表示结合受限生成，是精神科病历去标识的有效路径。
原文摘要 · Abstract (English)
Psychiatric narratives encode patient identity not only through explicit identifiers but also through idiosyncratic life events embedded in their clinical structure. Existing de-identification approaches, including PHI masking and LLM-based synthetic rewriting, operate at the text level and offer limited control over which semantic elements are preserved or altered. We introduce Anonpsy, a de-identification framework that reformulates the task as graph-guided semantic rewriting. Anonpsy (1) converts each narrative into a semantic graph encoding clinical entities, temporal anchors, and typed relations; (2) applies graph-constrained perturbations that modify identifying context while preserving clinically essential structure; and (3) regenerates text via graph-conditioned LLM generation. Evaluated on 90 clinician-authored psychiatric case narratives, Anonpsy preserves diagnostic fidelity while achieving consistently low re-identification risk under expert, semantic, and GPT-5-based evaluations. Compared with a strong LLM-only rewriting baseline, Anonpsy yields substantially lower semantic similarity and identifiability. These results demonstrate that explicit structural representations combined with constrained generation provide an effective approach to de-identification for psychiatric narratives.
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