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Related Experiment Video

Updated: Sep 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Clinical Laboratory Parameter-Driven Machine Learning for Participant Selection in Bioequivalence Studies Among

Byungeun Shon1, Sook Jin Seong2, Eun Jung Choi3

  • 1Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.

JMIR AI
|July 3, 2025
PubMed
Summary

A machine learning model effectively identifies eligible patients for clinical trials using laboratory data, significantly reducing recruitment workload and accelerating enrollment. This approach enhances clinical trial efficiency by improving participant selection.

Keywords:
AIKoreaMLartificial intelligenceclinical laboratory testclinical trialelectronic medical recordeligibility criteriaframeworkgastric cancermachine learningmodel developmentparticipant enrollmentpatient enrollmentsupporttrial

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Area of Science:

  • Biomedical Informatics
  • Clinical Trial Management
  • Machine Learning Applications

Background:

  • Clinical trial success is often hindered by insufficient participant enrollment.
  • Accurate and efficient patient identification is crucial for timely trial completion.

Purpose of the Study:

  • To develop a machine learning (ML)-based framework for identifying eligible participants for bioequivalence studies.
  • To leverage clinical laboratory parameters for optimizing clinical trial recruitment.

Main Methods:

  • Utilized electronic medical records of 11,592 gastric cancer patients.
  • Developed an ML model using 8 clinical laboratory parameters and acquisition dates.
  • Validated model performance using F1-score and Area Under the Curve (AUC) on training and test datasets.

Main Results:

  • The weighted ensemble ML model achieved an F1-score > 0.8 and AUC > 0.8.
  • Demonstrated high sensitivity in identifying valid candidates and minimizing misclassification.
  • Reduced screening workload by 57%, identifying 150 valid patients from 209, compared to 485 via random selection.

Conclusions:

  • The ML-based framework accurately identifies patients eligible for clinical trials.
  • This approach facilitates faster participant enrollment, addressing a key challenge in clinical research.
  • Clinical laboratory data can be effectively utilized to streamline patient recruitment processes.