Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study - Byungeun Shon; Sook Jin Seong; Eun Jung Choi; Mi-Ri Gwon; Hae Won Lee; Jaechan Park; Ho-Young Chung; Sungmoon Jeong; Young-Ran Yoon (JMIR AI)
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 a bioequivalence study. MethodsWe 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 ResultsThe weighted ensemble model achieved strong performance with an F1 ConclusionsThe proposed ML-based framework using clinical laboratory parameters can be used to identify patients eligible for a clinical trial, enabling faster participant enrollment.