  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 and intraoperative
features, we developed federated learning models to predict nine major
postoperative complications (i.e., prolonged intensive care unit stay and
mechanical ventilation). We compared federated learning models with local
learning models trained on a single site and central learning models trained on
pooled dataset from two centers. Results: Our federated learning models
achieved the area under the receiver operating characteristics curve (AUROC)
values ranged from 0.81 for wound complications to 0.92 for prolonged ICU stay
at UFH GNV center. At UFH JAX center, these values ranged from 0.73-0.74 for
wound complications to 0.92-0.93 for hospital mortality. Federated learning
models achieved comparable AUROC performance to central learning models, except
for prolonged ICU stay, where the performance of federated learning models was
slightly higher than central learning models at UFH GNV center, but slightly
lower at UFH JAX center. In addition, our federated learning model obtained
comparable performance to the best local learning model at each center,
demonstrating strong generalizability. Conclusion: Federated learning is shown
to be a useful tool to train robust and generalizable models from large scale
data across multiple institutions where data protection barriers are high.
