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

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.

Materials and methods

This retrospective cohort study used data from the electronic health records of adult surgical patients over 4 years (2018-2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood tra

SUBMITTER: Shen J 

PROVIDER: S-EPMC11833467 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature