Anonpsy: A Graph-Based Framework for Structure-Preserving De-identification of Psychiatric Narratives

• URL: http://arxiv.org/abs/2601.13503v1
• Date: Tue, 20 Jan 2026 01:37:44 GMT
• Title: Anonpsy: A Graph-Based Framework for Structure-Preserving De-identification of Psychiatric Narratives
• Abstract summary: We introduce Anonpsy, a de-identification framework that reformulates the task as graph-guided semantic rewriting.<n>Anonpsy converts each narrative into a semantic graph encoding clinical entities, temporal anchors, and typed relations.<n>It preserves diagnostic fidelity while achieving consistently low re-identification risk under expert, semantic, and GPT-5-based evaluations.
• Score: 1.4652274443334974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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