Electronic Medical Records are secure, private, lifetime records containing patient-health and point-of-care histories within the healthcare system, which can be used for disease surveillance, clinical studies, and many others. It is hard to share and use EMR data across jurisdictions because of the differences and barriers in data governance and architecture, as well as data security regulations. Federated learning is a new machine learning paradigm that trains machine learning models collaboratively without centralizing the data or exchanging data among clients, such as hospitals. Federated learning can broaden data used for analysis while honouring the required ethical and data sharing agreements.
Although sepsis is a major worldwide health problem, identifying sepsis remains challenging. The definitions for sepsis have been changing over the past 25 years. There is no gold standard definition for sepsis. Wide variation in incidence rates results from sepsis identification beginning with a pre-determined clinical definition and searching for cases in administrative and/or EMR data. Published work primarily utilize limited site-specific administrative data or registry data collected specifically for sepsis case-finding, presenting significant limitations on method generalization.
We propose to develop a novel Federated Learning framework for disease surveillance, with applications to the detection of sepsis, using electronic medical record data across multiple provinces in Canada without data sharing. We will evaluate the performance of the proposed framework through comparing with manual chart review, traditional locally trained machine learning models, as well as the commonly used coded definition of sepsis. The developed Federated Learning framework based on electronic medical records can be used to routinely detect sepsis for disease surveillance and monitoring and facilitate decision-making.
This webinar is presented in collaboration with Alberta Innovates and the LevMax Health Program.
Presented by:
Dr. Na Li is an Assistant Professor and early-career population health scientist in the Department of Community Health Sciences at the University of Calgary. Her research applies artificial intelligence (AI) technologies and methodologies to enhance learning health systems using high-quality, large-scale electronic healthcare data.
Dr. Li earned her PhD in Statistics from Western University in 2016 and completed her postdoctoral fellowship in the Department of Computing and Software at McMaster University in 2020. She joined the University of Calgary in 2021, focusing her research on bridging the gap between data-driven insights and practical healthcare solutions in health services research.
Earn One Continuing Education Unit (CEU) for attending this webinar. Content from the webinar aligns with Core Health Informatics Topics: The Canadian Healthcare System, Information Technology, Analysis and Evaluation
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