Prevention of stroke with its associated risk factors has been one of the
public health priorities worldwide. Emerging artificial intelligence technology
is being increasingly adopted to predict stroke. Because of privacy concerns,
patient data are stored in distributed electronic health record (EHR)
databases, voluminous clinical datasets, which prevent patient data from being
aggregated and restrains AI technology to boost the accuracy of stroke
prediction with centralized training data. In this work, our scientists and
engineers propose a privacy-preserving scheme to predict the risk of stroke and
deploy our federated prediction model on cloud servers. Our system of federated
prediction model asynchronously supports any number of client connections and
arbitrary local gradient iterations in each communication round. It adopts
federated averaging during the model training process, without patient data
being taken out of the hospitals during the whole process of model training and
forecasting. With the privacy-preserving mechanism, our federated prediction
model trains over all the healthcare data from hospitals in a certain city
without actual data sharing among them. Therefore, it is not only secure but
also more accurate than any single prediction model that trains over the data
only from one single hospital. Especially for small hospitals with few
confirmed stroke cases, our federated model boosts model performance by 10%~20%
in several machine learning metrics. To help stroke experts comprehend the
advantage of our prediction system more intuitively, we developed a mobile app
that collects the key information of patients' statistics and demonstrates
performance comparisons between the federated prediction model and the single
prediction model during the federated training process.

本研究提出了一个隐私保护方案，使用联合预测模型异步支持任意数量的客户端连接，采取联邦平均法训练模型，在不将患者数据带出医院的整个过程中，训练所有某一城市医院的医疗保健数据，以预测中风的风险，并开发一个移动应用程序，以演示联邦训练过程中联合预测模型和单一预测模型的性能比较。