A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients
Our advanced representation learning framework surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.
Authors
- Junbo Shen (ORCID: https://orcid.org/0009-0000-6259-7509)
- Chenyang Lu (ORCID: https://orcid.org/0000-0003-1709-6769)
- Bing Xue (ORCID: https://orcid.org/0000-0002-9162-098X)
- Thomas Kannampallil (ORCID: https://orcid.org/0000-0003-4119-4836)
- Joanna Abraham (ORCID: https://orcid.org/0000-0002-3576-5279)
Institutions
- Washington University in St. Louis (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-12-28
- DOI
- https://doi.org/10.1093/jamia/ocae316
- Citations
- 5
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.74