Cross-province federated machine learning of electronic health records in Canada

电子健康记录（EHR）系统在加拿大各省的迅速和广泛采用创造了丰富的数字医疗保健数据，并为进行变革性健康信息学研究创造了机会。快速发展的机器学习（ML）技术为疾病预测和监测中的大规模电子病历挖掘提供了巨大的希望。然而，电子病历数据访问一直是主要障碍，因为电子病历数据高度敏感，其使用受到严格监管。FL通过协作训练ML模型来避免数据访问问题，而无需在医院之间交换数据。我们提出了一种新的联邦学习（FL）框架，能够在来自加拿大3个省（即魁北克、安大略省和阿尔伯塔省）的加拿大医院的真实电子病历数据集上训练健壮的ML模型。我们将通过3个不同的目标来解决FL中的3个计算挑战：目标1：我们将开发一个健壮且保护隐私的FL，可部署到加拿大医疗保健系统；目标2：我们将解释由于来自3家省际医院的异质人群而导致的数据分布变化；目标3：我们将开发一个跨3个医疗保健部门的联合人口疾病自动化监测系统。我们提出的项目将极大地加速从局部健康信息学研究到全国医疗保健研究的范式转变。我们提出了培养下一代AI+健康研究人员的详细培训计划。HQP将由团队成员在各自的地点（麦吉尔大学、卡尔加里大学和不列颠哥伦比亚省）进行监督，并进行夏季交流访问。我们将举办实验室间每月联合会议，以丰富学员的跨学科研究经验。由于FL在学术界和工业界的趋势，HQP在完成拟议的FL-in-health项目中获得的技能在就业市场上受到高度追捧。

The rapid and broad adoption of electronic health records (EHR) systems across provinces in Canada has created rich digital healthcare data and opportunities for conducting transformative health informatics research. Rapidly advancing machine learning (ML) technologies offer great promise for large-scale EHR mining in disease prediction and surveillance. However, EHR data access has been the main hurdle because EHR data are highly sensitive, and their usage is tightly regulated. FL circumvents the data access problem by training ML models collaboratively without exchanging the data among the hospitals. We propose a novel Federated Learning (FL) framework to enable training robust ML models on real-world EHR datasets from Canadian hospitals spanning across 3 provinces in Canada, namely Quebec, Ontario, and Alberta. We will address 3 computational challenges in FL with 3 separate Aims: Aim 1: We will develop a robust and privacy-preserving FL that is deployable to the Canadian healthcare systems; Aim 2: We will account for the data distribution shifts due to heterogeneous populations from the 3 inter-provincial hospitals; Aim 3: We will develop a federated automated population disease surveillance system across the 3 healthcare sectors. Our proposed project will greatly accelerate the paradigm shift from local piece-wise health informatics research to nation-wide healthcare research. We propose a detailed training plan to train the next-generation AI+Health researchers. The HQP will be supervised by team members at their sites (McGill University, University of Calgary, and UBC) with summer exchange visits. We will host joint monthly meeting among the labs to enrich trainees' interdisciplinary research experience. The skills acquired by the HQP in completing the proposed FL-in-health project are highly sought in the job market due to the trend of FL in both academia and industry.