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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| Format: | Article |
| Language: | English |
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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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| author | Byungeun Shon Sook Jin Seong Eun Jung Choi Mi-Ri Gwon Hae Won Lee Jaechan Park Ho-Young Chung Sungmoon Jeong Young-Ran Yoon |
| author_facet | Byungeun Shon Sook Jin Seong Eun Jung Choi Mi-Ri Gwon Hae Won Lee Jaechan Park Ho-Young Chung Sungmoon Jeong Young-Ran Yoon |
| author_sort | Byungeun Shon |
| collection | DOAJ |
| description |
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. |
| format | Article |
| id | doaj-art-ad54543faa9949bd81fa183edd17decc |
| institution | Kabale University |
| issn | 2817-1705 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR AI |
| spelling | doaj-art-ad54543faa9949bd81fa183edd17decc2025-08-20T03:28:55ZengJMIR PublicationsJMIR AI2817-17052025-05-014e64845e6484510.2196/64845Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation StudyByungeun Shonhttp://orcid.org/0000-0001-5161-2398Sook Jin Seonghttp://orcid.org/0000-0001-8872-3796Eun Jung Choihttp://orcid.org/0000-0003-1881-9869Mi-Ri Gwonhttp://orcid.org/0000-0001-5703-1615Hae Won Leehttp://orcid.org/0000-0001-7299-5332Jaechan Parkhttp://orcid.org/0000-0001-7572-3260Ho-Young Chunghttp://orcid.org/0000-0001-7264-0865Sungmoon Jeonghttp://orcid.org/0000-0002-4579-3150Young-Ran Yoonhttp://orcid.org/0000-0003-2166-7579 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.https://ai.jmir.org/2025/1/e64845 |
| spellingShingle | Byungeun Shon Sook Jin Seong Eun Jung Choi Mi-Ri Gwon Hae Won Lee Jaechan Park Ho-Young Chung Sungmoon Jeong Young-Ran Yoon Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study JMIR AI |
| title | Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study |
| title_full | Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study |
| title_fullStr | Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study |
| title_full_unstemmed | Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study |
| title_short | Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study |
| title_sort | clinical laboratory parameter driven machine learning for participant selection in bioequivalence studies among patients with gastric cancer framework development and validation study |
| url | https://ai.jmir.org/2025/1/e64845 |
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