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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-05-01
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| Series: | JMIR AI |
| Online Access: | https://ai.jmir.org/2025/1/e64845 |
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| Summary: | 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. |
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| ISSN: | 2817-1705 |