The SURROGATOR Framework for Context-Aware Surrogation of Privacy Sensitive Information in Medical Text
Lohr C, Seiferling M, Wiesenbach P, Faller J, Dieterich C (2026)
Publication Type: Journal article
Publication year: 2026
Journal
Book Volume: 336
Pages Range: 1405-1409
DOI: 10.3233/SHTI260440
Abstract
Clinical text cannot be shared freely due to data protection regulations. This paper introduces SURROGATOR, an open-source framework that replaces personal identifiable information with fictitious, high-fidelity synthetic surrogates to maintain text utility for human and artificial intelligence. We focus on the automated replacement of names, dates, and locations. Evaluation on a synthetic clinical corpus demonstrated high data utility, with named entity recognition performance remaining en par with the original text (F1-score of 0.70 vs. 0.73). Furthermore, a re-identification attack using a large language model resulted in an accuracy of 50.8%, which is equivalent to random chance. We conclude that SURROGATOR effectively balances patient privacy and data utility, providing a robust solution for the secure sharing of clinical documents in medical research.
Involved external institutions
Germany (DE)How to cite
APA:
Lohr, C., Seiferling, M., Wiesenbach, P., Faller, J., & Dieterich, C. (2026). The SURROGATOR Framework for Context-Aware Surrogation of Privacy Sensitive Information in Medical Text. Studies in Health Technology and Informatics, 336, 1405-1409. https://doi.org/10.3233/SHTI260440
MLA:
Lohr, Christina, et al. "The SURROGATOR Framework for Context-Aware Surrogation of Privacy Sensitive Information in Medical Text." Studies in Health Technology and Informatics 336 (2026): 1405-1409.
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