Using machine learning to identify key subject categories predicting the pre-clerkship and clerkship performance: 8-year cohort study.

Journal: Journal of the Chinese Medical Association : JCMA
Published Date: Apr 18, 2024

Abstract

BACKGROUND: Medical students need to build a solid foundation of knowledge to become physicians. Clerkship is often considered the first transition point, and clerkship performance is essential for their development. We hope to identify subjects that could predict the clerkship performance, thus helping medical students learn more efficiently to achieve high clerkship performance.

Authors

  • Shiau-Shian Huang
    Department of Medical Education, Clinical Innovation Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
  • Yu-Fan Lin
    Department of Medical Education, Clinical Innovation Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
  • Anna YuQing Huang
    Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan, ROC.
  • Ji-Yang Lin
    Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan, ROC.
  • Ying-Ying Yang
    Division of Clinical Skills Training and High-fidelity Medical Simulation for Holistic Care and Inter-Professional Collaboration, Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan.
  • Sheng-Min Lin
    Department of Medical Education, Clinical Innovation Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
  • Wen-Yu Lin
    Department of Medical Education, Clinical Innovation Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
  • Pin-Hsiang Huang
    Department of Medical Education, Clinical Innovation Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
  • Tzu-Yao Chen
    Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan, ROC.
  • Stephen J H Yang
    Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan, ROC.
  • Jiing-Feng Lirng
    Department of Medical Education, Clinical Innovation Center, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
  • Chen-Huan Chen
    National Yang-Ming University, Taipei, Taiwan.

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