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

Background: Insufficient participant enrollment is a major factor responsible for clinical trial failure. Objective: We formulated a machine learning (ML)–based framework using clinical laboratory parameters to identify participants eligible for enrollment in a bioequivalence study. Methods: We acquired records of 11,592 patients with gastric cancer from the electronic medical records of Kyungpook National University Hospital in Korea. The ML model was developed using 8 clinical laboratory parameters, including complete blood count and liver and kidney function tests, along with the dates of acquisition. Two datasets were collected: (1) a training dataset to design an ML-based candidate selection method and (2) a test dataset to evaluate the performance of the proposed method. The generalization performance of the ML-based method was confirmed using the F₁-score and the area under the curve (AUC). The proposed model was compared with a random selection method to evaluate its efficacy in recruiting participants. Results: The weighted ensemble model achieved strong performance with an F₁-score above 0.8 and an AUC value exceed-ing 0.8, demonstrating its ability to accurately identify valid clinical trial candidates while minimizing misclassification. Its high sensitivity further enhanced the model’s efficiency in prioritizing patients for screening. In a case study, the proposed ML model reduced the workload by 57%, efficiently identifying 150 valid patients from a pool of 209, compared to the 485 patients required by random selection. Conclusions: The proposed ML-based framework using clinical laboratory parameters can be used to identify patients eligible for a clinical trial, enabling faster participant enrollment.