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

AI for Synthetic Data (24) Research Instruments, Questionnaires, and Tools (1177) Electronic Health Records (1685) Theoretical Innovations in AI (32) Generative Language Models Including ChatGPT (1455) AI Language Models in Health Care (713)

Published on in Vol 5 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/90590, first published .
Laptop displaying a medical record form with fields for personal information.

Improving Clinical Validity in Synthetic Electronic Health Record Generation Using Best-of-N Sampling: Comparative Evaluation Study

Improving Clinical Validity in Synthetic Electronic Health Record Generation Using Best-of-N Sampling: Comparative Evaluation Study

Authors of this article:

Md Akmol Masud1 Author Orcid Image ;   Mahmud Hasan2 Author Orcid Image
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Md Akmol Masud   1 , BSc ;   Mahmud Hasan   2 , PhD

1 Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada

2 Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, United States

Corresponding Author:

  • Md Akmol Masud, BSc
  • Department of Electrical and Computer Engineering
  • Queen's University
  • 99 University Ave
  • Kingston, ON K7L 3N6
  • Canada
  • Phone: +880 1304963440
  • Email: m.masud@queensu.ca