@inproceedings{wu-etal-2026-privacy,
title = "Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies",
author = "Wu, Yuping and
Schlegel, Viktor and
Del-Pinto, Warren and
Nandakumar, Srinivasan and
Zahid, Iqra and
Sun, Yidan and
Li, Hao and
Omar, Usama Farghaly and
Jasmine, Amirah and
Kaliya-Perumal, Arun-Kumar and
Tham, Chun Shen and
Connors, Gabriel and
Bharath, Anil Anthony and
Nenadic, Goran",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2107/",
pages = "47949--47969",
ISBN = "979-8-89176-459-0",
abstract = "In high-stakes domains such as healthcare, privacy concerns severely limit the use of real-world training data. Differentially private (DP) synthetic data offers a promising alternative with formal privacy guarantees, but achieving strong utility remains challenging for clinical note generation due to domain specificity and long-form text complexity. We present Term2Note, a method for synthesising full-length clinical notes under strong DP constraints. By structurally separating content and form, Term2Note generates section-wise note content conditioned on medical terms, with terms and notes privatised under separate DP constraints, and applies a DP quality maximiser to improve outputs. Experiments demonstrate that Term2Note produces synthetic notes with statistical properties closely aligned with real clinical notes, and that downstream models trained on these notes achieve performance comparable to those trained on real clinical data. Compared to existing DP text generation baselines, Term2Note substantially improves both fidelity and utility, without relying on label distribution assumptions, highlighting its effectiveness as a practical privacy-preserving alternative to real clinical notes."
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<abstract>In high-stakes domains such as healthcare, privacy concerns severely limit the use of real-world training data. Differentially private (DP) synthetic data offers a promising alternative with formal privacy guarantees, but achieving strong utility remains challenging for clinical note generation due to domain specificity and long-form text complexity. We present Term2Note, a method for synthesising full-length clinical notes under strong DP constraints. By structurally separating content and form, Term2Note generates section-wise note content conditioned on medical terms, with terms and notes privatised under separate DP constraints, and applies a DP quality maximiser to improve outputs. Experiments demonstrate that Term2Note produces synthetic notes with statistical properties closely aligned with real clinical notes, and that downstream models trained on these notes achieve performance comparable to those trained on real clinical data. Compared to existing DP text generation baselines, Term2Note substantially improves both fidelity and utility, without relying on label distribution assumptions, highlighting its effectiveness as a practical privacy-preserving alternative to real clinical notes.</abstract>
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%0 Conference Proceedings
%T Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies
%A Wu, Yuping
%A Schlegel, Viktor
%A Del-Pinto, Warren
%A Nandakumar, Srinivasan
%A Zahid, Iqra
%A Sun, Yidan
%A Li, Hao
%A Omar, Usama Farghaly
%A Jasmine, Amirah
%A Kaliya-Perumal, Arun-Kumar
%A Tham, Chun Shen
%A Connors, Gabriel
%A Bharath, Anil Anthony
%A Nenadic, Goran
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-privacy
%X In high-stakes domains such as healthcare, privacy concerns severely limit the use of real-world training data. Differentially private (DP) synthetic data offers a promising alternative with formal privacy guarantees, but achieving strong utility remains challenging for clinical note generation due to domain specificity and long-form text complexity. We present Term2Note, a method for synthesising full-length clinical notes under strong DP constraints. By structurally separating content and form, Term2Note generates section-wise note content conditioned on medical terms, with terms and notes privatised under separate DP constraints, and applies a DP quality maximiser to improve outputs. Experiments demonstrate that Term2Note produces synthetic notes with statistical properties closely aligned with real clinical notes, and that downstream models trained on these notes achieve performance comparable to those trained on real clinical data. Compared to existing DP text generation baselines, Term2Note substantially improves both fidelity and utility, without relying on label distribution assumptions, highlighting its effectiveness as a practical privacy-preserving alternative to real clinical notes.
%U https://aclanthology.org/2026.emnlp-main.2107/
%P 47949-47969
Markdown (Informal)
[Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies](https://aclanthology.org/2026.emnlp-main.2107/) (Wu et al., EMNLP 2026)
ACL
- Yuping Wu, Viktor Schlegel, Warren Del-Pinto, Srinivasan Nandakumar, Iqra Zahid, Yidan Sun, Hao Li, Usama Farghaly Omar, Amirah Jasmine, Arun-Kumar Kaliya-Perumal, Chun Shen Tham, Gabriel Connors, Anil Anthony Bharath, and Goran Nenadic. 2026. Privacy-Preserving Generation of Clinical Narratives from Medical Terminologies. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47949–47969, Budapest, Hungary. Association for Computational Linguistics.