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Mullie L, Afilalo J, Archambault P, Bouchakri R, Brown K, Buckeridge DL, Cavayas YA, Turgeon AF, Martineau D, Lamontagne F, Lebrasseur M, Lemieux R, Li J, Sauthier M, St-Onge P, Tang A, Witteman W, Chassé M. CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data. J Am Med Inform Assoc 2024; 31:651-665. [PMID: 38128123 PMCID: PMC10873779 DOI: 10.1093/jamia/ocad235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/28/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVES Distributed 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 METHODS We 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. RESULTS The 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 CONCLUSION The 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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Affiliation(s)
- Louis Mullie
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
| | - Jonathan Afilalo
- Department of Medicine, Jewish General Hospital, Montréal, H3T 1E4, Canada
| | - Patrick Archambault
- Department of Emergency Medicine and Family Medicine, Université Laval, Québec, G1V 0A6, Canada
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Rima Bouchakri
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Kip Brown
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - David L Buckeridge
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University Health Centre, Montréal, H3A 1G1, Canada
| | | | - Alexis F Turgeon
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - Denis Martineau
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - François Lamontagne
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Martine Lebrasseur
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Renald Lemieux
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Jeffrey Li
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Michaël Sauthier
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Pediatrics, Université de Montréal and CHU Sainte-Justine Research Centre, Montréal, H3C 3J7, Canada
| | - Pascal St-Onge
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - An Tang
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Radiology, Centre Hospitalier de l’Université de Montréal, Montréal, H2X 3E4, Canada
| | - William Witteman
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Michaël Chassé
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
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Sauthier N, Bouchakri R, Carrier FM, Sauthier M, Mullie LA, Cardinal H, Fortin MC, Lahrichi N, Chassé M. Automated screening of potential organ donors using a temporal machine learning model. Sci Rep 2023; 13:8459. [PMID: 37231073 DOI: 10.1038/s41598-023-35270-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 05/15/2023] [Indexed: 05/27/2023] Open
Abstract
Organ donation is not meeting demand, and yet 30-60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949-0.981) for the neural network and 0.940 (0.908-0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.
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Affiliation(s)
- Nicolas Sauthier
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Rima Bouchakri
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | | | - Michaël Sauthier
- Centre Hospitalier Universitaire Sainte-Justine, Montreal, Canada
| | | | - Héloïse Cardinal
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | | | | | - Michaël Chassé
- Centre Hospitalier de l'Université de Montréal, Montreal, Canada.
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Abstract
Michael Yu and colleagues examine the challenges in developing AI tools for use at point of care
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Affiliation(s)
- Michael Yu
- Center for Integration and Analysis of Medical Data (CITADEL), Department of Medicine, University of Montreal, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - An Tang
- Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Canada
| | - Kip Brown
- Center for Integration and Analysis of Medical Data (CITADEL), Department of Medicine, University of Montreal, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Rima Bouchakri
- Center for Integration and Analysis of Medical Data (CITADEL), Department of Medicine, University of Montreal, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Pascal St-Onge
- Center for Integration and Analysis of Medical Data (CITADEL), Department of Medicine, University of Montreal, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Sheng Wu
- World Health Organization, Geneva, Switzerland
| | - John Reeder
- World Health Organization, Geneva, Switzerland
| | - Louis Mullie
- Center for Integration and Analysis of Medical Data (CITADEL), Department of Medicine, University of Montreal, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
| | - Michaël Chassé
- Center for Integration and Analysis of Medical Data (CITADEL), Department of Medicine, University of Montreal, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Canada
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