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This paper is in the following e-collection/theme issue:

Methods and Instruments in Medical Informatics (435) Clinical Information and Decision Making (3866) Electronic Health Records (1786) Mental Health Issues in Adolescence (792) AI for Synthetic Data (27)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71819, first published .
Scales balancing synthetic data generation (shield lock) vs. AI/brain (charts)

Balancing Privacy and Utility in Child and Adolescent Mental Health Services Research: Retrospective Cohort Study on Synthetic Data Generation

Balancing Privacy and Utility in Child and Adolescent Mental Health Services Research: Retrospective Cohort Study on Synthetic Data Generation

Authors of this article:

Mounir Haizoune1 Author Orcid Image ;   Bennett L Leventhal2 Author Orcid Image ;   Dipendra Pant3, 4 Author Orcid Image ;   Øystein Nytrø3, 4 Author Orcid Image ;   Kaban Koochakpour3, 4 Author Orcid Image ;   Roman A Koposov5 Author Orcid Image ;   Lars Ravn Øhlckers6 Author Orcid Image ;   Norbert Skokauskas3, 7 Author Orcid Image
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Journals

  1. Kamdje Wabo G, Sokolowski P, Jannesari Ladani M, Hagmann M, Ganslandt T, Siegel F. Quantifying the Impact of Anonymization-Induced Clinical Data Quality Loss: Methodological Quantitative Case Study Using Primary Diagnosis Codes and Hospital Length of Stay. JMIR Medical Informatics 2026;14:e97661 View