Machine Learning Approach for Active Vaccine Safety Monitoring.

Journal: Journal of Korean medical science
PMID: 34402232

Abstract

BACKGROUND: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active surveillance, we developed and verified a machine learning-based active surveillance system using national claim data.

Authors

  • Yujeong Kim
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Korea.
  • Jong Hwan Jang
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yongin, Korea.
  • Namgi Park
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Gyeonggi-do, Republic of Korea.
  • Na Young Jeong
    Department of Health Convergence, Ewha Womans University, Seoul, Korea.
  • Eunsun Lim
    Department of Health Convergence, Ewha Womans University, Seoul, Korea.
  • Soyun Kim
    Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
  • Nam Kyong Choi
    Department of Health Convergence, Ewha Womans University, Seoul, Korea.
  • Dukyong Yoon
    Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea.

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