Federated learning model for predicting major postoperative complications

arXiv:2404.06641v1 Announce Type: new
Abstract: Background: The accurate prediction of postoperative complication risk using Electronic Health Records (EHR) and artificial intelligence shows great potential. Training a robust artificial intelligence model typically requires large-scale and diverse datasets. In reality, collecting medical data often encounters challenges surrounding privacy protection. Methods: This retrospective cohort study includes adult patients who were admitted to UFH Gainesville (GNV) (n = 79,850) and Jacksonville (JAX) (n = 28,636) for any type of inpatient surgical procedure. Using perioperative …

abstract artificial artificial intelligence arxiv challenges cs.ai cs.cy cs.lg data datasets diverse ehr electronic electronic health records federated learning health intelligence major medical medical data prediction privacy protection reality records retrospective risk robust scale shows study training type