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

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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.