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arXiv Computation and Language By Stig Hellemans, Tom Stroobants, Elyne Scheurwegs, Pieter Meysman, Philippe G. Jorens, Kris Laukens

MedDeID enables locally governed clinical-text de-identification from real or synthetic training data

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MedDeID is an on‑premises framework that combines in‑house annotation, synthetic‑note generation, model training, inference, pseudonymisation and evaluation to de‑identify clinical text. On a Dutch hospital benchmark, a hospital‑trained transformer detected 98.9 % of identifying text while redacting only 0.24 % of non‑identifier text; a synthetic‑only model achieved 96.1 %. In primary‑care notes, the synthetic‑trained model outperformed the hospital‑trained model in recall and robustness to identifier‑format changes, and an English version trained without real text reached 99.7 % and 98.9 % detection on synthetic benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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