Deep Learning-Based Dynamic Risk Prediction of Venous Thromboembolism for Patients With Ovarian Cancer in Real-World Settings From Electronic Health Records.

Journal: JCO clinical cancer informatics
PMID: 38996199

Abstract

PURPOSE: Patients with epithelial ovarian cancer (EOC) have an elevated risk for venous thromboembolism (VTE). To assess the risk of VTE, models were developed by statistical or machine learning algorithms. However, few models have accommodated deep learning (DL) algorithms in realistic clinical settings. We aimed to develop a predictive DL model, exploiting rich information from electronic health records (EHRs), including dynamic clinical features and the presence of competing risks.

Authors

  • Dahhay Lee
    Department of Cancer AI and Digital Health, National Cancer Center Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Republic of Korea.
  • Seongyoon Kim
    School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea.
  • Sanghee Lee
    Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA.
  • Hak Jin Kim
    Department of Cardiology, Gumdan Top General Hospital, Incheon, Republic of Korea.
  • Ji Hyun Kim
    Center for Gynecologic Cancer, National Cancer Center, Goyang, Republic of Korea.
  • Myong Cheol Lim
    Center for Gynecologic Cancer, National Cancer Center, Research Institute and Hospital, Goyang, South Korea. mclim@ncc.re.kr.
  • Hyunsoon Cho
    Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea.

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