Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study
The proposed ML-based framework using clinical laboratory parameters can be used to identify patients eligible for a clinical trial, enabling faster participant enrollment.
Authors
- Jaechan Park (ORCID: https://orcid.org/0000-0001-7572-3260)
- Hae Won Lee (ORCID: https://orcid.org/0000-0001-7299-5332)
- Sungmoon Jeong (ORCID: https://orcid.org/0000-0002-4579-3150)
- Young‐Ran Yoon (ORCID: https://orcid.org/0000-0003-2166-7579)
- Sook Jin Seong
- Ho Young Chung (ORCID: https://orcid.org/0000-0001-7264-0865)
- Eun Jung Choi (ORCID: https://orcid.org/0000-0003-1881-9869)
- Mi‐Ri Gwon (ORCID: https://orcid.org/0000-0001-5703-1615)
- Byungeun Shon (ORCID: https://orcid.org/0000-0001-5161-2398)
Institutions
- Kyungpook National University (KR)
- Kyungpook National University Medical Center (KR)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-05-05
- DOI
- https://doi.org/10.2196/64845
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00