预测术后主要并发症的联邦学习模型
Federated learning model for predicting major postoperative complications
AI总结：
本研究基于两家医院围手术期EHR构建联邦学习模型预测九类术后主要并发症，在保护隐私的同时达到接近集中式训练和最佳局部模型的性能。
摘要：
背景：利用电子健康记录（EHR）和人工智能准确预测术后并发症风险展现出巨大潜力。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。在现实中，收集医疗数据往往面临围绕隐私保护的挑战。方法：这项回顾性队列研究纳入了因任何类型住院手术而入住UFH Gainesville（GNV）（n=79,850）和Jacksonville（JAX）（n=28,636）的成年患者。利用围手术期和术中特征，我们开发了联邦学习模型，用于预测九种主要术后并发症（即重症监护室停留时间延长和机械通气）。我们将联邦学习模型与在单一场点训练的局部学习模型以及在来自两个中心的汇总数据集上训练的集中式学习模型进行了比较。结果：我们的联邦学习模型在UFH GNV中心取得的受试者工作特征曲线下面积（AUROC）值从伤口并发症的0.81到ICU停留延长的0.92不等。在UFH JAX中心，这些值从伤口并发症的0.73–0.74到院内死亡的0.92–0.93不等。联邦学习模型取得了与集中式学习模型相当的AUROC性能，但ICU停留延长除外：在该并发症上，联邦学习模型在UFH GNV中心的性能略高于集中式学习模型，而在UFH JAX中心略低。此外，我们的联邦学习模型在每个中心都取得了与最佳局部学习模型相当的性能，展现出很强的泛化能力。结论：联邦学习被证明是一种有用的工具，可在数据保护壁垒较高的情况下，利用跨多个机构的大规模数据训练稳健且可泛化的模型。
英文摘要：
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.
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