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

Applications of AI (246) Drug Discovery and Clinical Trials (5) Innovations in Clinical Trials and Research Data Management (117) Machine Learning (1971) Secondary Use of Clinical Data for Research and Surveillance (453) Recruitment of Research Participants (365)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64845, first published .
Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study

Authors of this article:

Byungeun Shon1 Author Orcid Image ;   Sook Jin Seong2 Author Orcid Image ;   Eun Jung Choi3 Author Orcid Image ;   Mi-Ri Gwon3 Author Orcid Image ;   Hae Won Lee3 Author Orcid Image ;   Jaechan Park4 Author Orcid Image ;   Ho-Young Chung1 Author Orcid Image ;   Sungmoon Jeong1 Author Orcid Image ;   Young-Ran Yoon3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

There are no citations yet available for this article according to Crossref .