Synthetic Data Improve Survival Status Prediction Models in Early-Onset Colorectal Cancer.

Journal: JCO clinical cancer informatics
Published Date: Jan 1, 2024

Abstract

PURPOSE: In artificial intelligence-based modeling, working with a limited number of patient groups is challenging. This retrospective study aimed to evaluate whether applying synthetic data generation methods to the clinical data of small patient groups can enhance the performance of prediction models.

Authors

  • Hyunwook Kim
    Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
  • Won Seok Jang
    Miner School of Computer & Information Sciences, University of Massachusetts Lowell, Lowell, MA.
  • Woo Seob Sim
    Medical Informatics Collaboration Unit, Department of Research Affairs, Yonsei University College of Medicine, Seoul, South Korea.
  • Han Sang Kim
    Department of Internal Medicine, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, Korea. modeerfhs@yuhs.ac.
  • Jeong Eun Choi
    Office of Data Services at Division of Digital Health, Yonsei University Health System, Seoul, South Korea.
  • Eun Sil Baek
    Songdang Institute for Cancer Research, Yonsei University College of Medicine, Seoul, South Korea.
  • Yu Rang Park
    Asan Medical Center, Seoul, Republic of Korea.
  • Sang Joon Shin
    Division of Medical Oncology, Department of Internal Medicine, Yonsei Cancer Center, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.

Keywords

External Resources

Popular Topics
Recent Journals