A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.
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.
Author(s): Shen, Junbo, Xue, Bing, Kannampallil, Thomas, Lu, Chenyang, Abraham, Joanna
DOI: 10.1093/jamia/ocae316