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.

翻译：背景：利用电子健康记录（EHR）和人工智能精准预测术后并发症风险具有巨大潜力。训练稳健的人工智能模型通常需要大规模且多样化的数据集。然而在现实中，医学数据采集常面临隐私保护的挑战。方法：本回顾性队列研究纳入在UFH盖恩斯维尔（GNV）（n=79,850）和杰克逊维尔（JAX）（n=28,636）接受各类住院手术的成年患者。基于围术期和术中特征，我们开发了联邦学习模型以预测九种主要术后并发症（即重症监护室住院时间延长和机械通气）。我们将联邦学习模型与基于单中心数据训练的本地学习模型以及基于两中心合并数据集训练的中央学习模型进行了比较。结果：在UFH GNV中心，我们的联邦学习模型接受者操作特征曲线下面积（AUROC）值范围为0.81（伤口并发症）至0.92（ICU住院时间延长）。在UFH JAX中心，该值范围为0.73-0.74（伤口并发症）至0.92-0.93（院内死亡率）。除ICU住院时间延长外（在UFH GNV中心联邦学习模型AUROC略高于中央学习模型，而在UFH JAX中心略低），联邦学习模型与中央学习模型取得了相当的AUROC性能。此外，联邦学习模型在各中心均取得了与最优本地学习模型相当的性能，展现出强大的泛化能力。结论：联邦学习被证明是有效的工具，可在数据保护要求较高的多机构场景下，利用大规模数据训练稳健且具有泛化能力的模型。