Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study
Abstract BackgroundInsufficient participant enrollment is a major factor responsible for clinical trial failure. ObjectiveWe formulated a machine learning (ML)–based framework using clinical laboratory parameters to identify participants eligible for enrollment in...
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| Główni autorzy: | , , , , , , , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
JMIR Publications
2025-05-01
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| Seria: | JMIR AI |
| Dostęp online: | https://ai.jmir.org/2025/1/e64845 |
| Etykiety: |
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