Abstract
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.
Abstract (translated)
精神健康叙述通过明确的身份标识符以及嵌入在临床结构中的个人化生活事件来编码患者身份。现有的去识别方法，包括PHI屏蔽和基于LLM的合成重写，在文本层面操作，并且对保留或更改哪些语义元素控制有限。我们引入了Anonpsy，这是一种以引导语义重写的图谱为指导的去标识框架。Anonpsy（1）将每个叙述转换成一个包含临床实体、时间锚点和类型化关系的语义图；（2）应用受约束的图谱修改，改变识别性上下文的同时保留临床结构的关键部分；（3）通过条件生成的LLM再生文本。在对90个由精神科医生撰写的案例叙述进行评估时，Anonpsy在保持诊断准确性的同时，在专家、语义和GPT-5基线测试中均表现出一致低水平的再识别风险。与一个强大的基于LLM的重写基准相比，Anonpsy显著降低了语义相似性和可标识性。这些结果表明，结合显式结构表示和受约束生成的方法为精神健康叙述的去标识提供了有效途径。
URL
https://arxiv.org/abs/2601.13503
