A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39731515

Abstract

OBJECTIVE: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.

Authors

  • Junbo Shen
    Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO 63130, United States.
  • Bing Xue
  • Thomas Kannampallil
    Department of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, USA.
  • Chenyang Lu
    Department of Computer Science and Engineering, Washington University, St. Louis, MO.
  • Joanna Abraham
    Department of Anesthesiology, Washington University in St Louis, St Louis, MO, 63110, USA.

Keywords

External Resources

Popular Topics
Recent Journals