ArticleJournal of the American Medical Informatics Association : JAMIA2024
CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Article
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- Navigating real-world challenges: A case study on federated learning in computational pathology.Journal of pathology informatics · 2025Article
- Data Interoperability in Context: The Importance of Open-Source Implementations When Choosing Open Standards.Journal of medical Internet research · 2025Article
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
- Real-world federated learning in radiology: hurdles to overcome and benefits to gain.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Trauma systems in high socioeconomic index countries in 2050.Critical care (London, England) · 2024Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
objectivesDistributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. MATERIALS AND
methodsWe describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations.
resultsThe CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. DISCUSSION AND
conclusionThe CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.