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Sinaci AA, Gencturk M, Alvarez-Romero C, Laleci Erturkmen GB, Martinez-Garcia A, Escalona-Cuaresma MJ, Parra-Calderon CL. Privacy-preserving federated machine learning on FAIR health data: A real-world application. Comput Struct Biotechnol J 2024; 24:136-145. [PMID: 38434250 PMCID: PMC10904920 DOI: 10.1016/j.csbj.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
Objective This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. Materials and methods Utilizing an agent-based architecture, a privacy-preserving federated ML algorithm was developed to create a global predictive model from various local models. This involved formally defining the algorithm in two steps: data preparation and federated model training on FAIR health data and constructing the architecture with multiple components facilitating algorithm execution. The solution was validated by five healthcare organizations using their specific health datasets. Results Five organizations transformed their datasets into Health Level 7 Fast Healthcare Interoperability Resources via a common FAIRification workflow and software set, thereby generating FAIR datasets. Each organization deployed a Federated ML Agent within its secure network, connected to a cloud-based Federated ML Manager. System testing was conducted on a use case aiming to predict 30-day readmission risk for chronic obstructive pulmonary disease patients and the federated model achieved an accuracy rate of 87%. Discussion The paper demonstrated a practical application of privacy-preserving federated ML among five distinct healthcare entities, highlighting the value of FAIR health data in machine learning when utilized in a federated manner that ensures privacy protection without sharing data. Conclusion This solution effectively leverages FAIR datasets from multiple healthcare organizations for federated ML while safeguarding sensitive health datasets, meeting legislative privacy and security requirements.
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Affiliation(s)
- A. Anil Sinaci
- SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey
| | - Mert Gencturk
- SRDC Software Research Development and Consultancy Corporation, Ankara, Turkey
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Celia Alvarez-Romero
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | | | - Alicia Martinez-Garcia
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
| | | | - Carlos Luis Parra-Calderon
- Group of Research and Innovation in Biomedical Informatics, Biomedical Engineering and Health Economy, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, Spain
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Eminaga O, Lee TJ, Ge J, Shkolyar E, Laurie M, Long J, Hockman LG, Liao JC. Conceptual framework and documentation standards of cystoscopic media content for artificial intelligence. J Biomed Inform 2023; 142:104369. [PMID: 37088456 PMCID: PMC10643098 DOI: 10.1016/j.jbi.2023.104369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 04/03/2023] [Accepted: 04/18/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. METHODS A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure compliance with FAIR principles. RESULTS The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion, and frame levels. CONCLUSION Our study shows that the proposed framework facilitates the storage of visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.
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Affiliation(s)
- Okyaz Eminaga
- Department of Urology, Stanford University School of Medicine, Stanford, USA; Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA, USA.
| | - Timothy Jiyong Lee
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Jessie Ge
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Mark Laurie
- Department of Urology, Stanford University School of Medicine, Stanford, USA
| | - Jin Long
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Joseph C Liao
- Department of Urology, Stanford University School of Medicine, Stanford, USA; Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA, USA.
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Martínez-García A, Alvarez-Romero C, Román-Villarán E, Bernabeu-Wittel M, Luis Parra-Calderón C. FAIR principles to improve the impact on health research management outcomes. Heliyon 2023; 9:e15733. [PMID: 37205991 PMCID: PMC10189186 DOI: 10.1016/j.heliyon.2023.e15733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/21/2023] Open
Abstract
Background The FAIR principles, under the open science paradigm, aim to improve the Findability, Accessibility, Interoperability and Reusability of digital data. In this sense, the FAIR4Health project aimed to apply the FAIR principles in the health research field. For this purpose, a workflow and a set of tools were developed to apply FAIR principles in health research datasets, and validated through the demonstration of the potential impact that this strategy has on health research management outcomes. Objective This paper aims to describe the analysis of the impact on health research management outcomes of the FAIR4Health solution. Methods To analyse the impact on health research management outcomes in terms of time and economic savings, a survey was designed and sent to experts on data management with expertise in the use of the FAIR4Health solution. Then, differences between the time and costs needed to perform the techniques with (i) standalone research, and (ii) using the proposed solution, were analyzed. Results In the context of the health research management outcomes, the survey analysis concluded that 56.57% of the time and 16800 EUR per month could be saved if the FAIR4Health solution is used. Conclusions Adopting principles in health research through the FAIR4Health solution saves time and, consequently, costs in the execution of research involving data management techniques.
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Affiliation(s)
- Alicia Martínez-García
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain
| | - Celia Alvarez-Romero
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain
- Corresponding author.
| | - Esther Román-Villarán
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain
| | | | - Carlos Luis Parra-Calderón
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain
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Eminaga O, Lee TJ, Ge J, Shkolyar E, Laurie M, Long J, Hockman LG, Liao JC. Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence. ARXIV 2023:arXiv:2301.05991v2. [PMID: 36713258 PMCID: PMC9882574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice. METHODS A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, re-usable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure the compliance with FAIR principles. RESULTS The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion and frame levels. CONCLUSION Our study shows that the proposed framework facilitates the storage of the visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.
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Affiliation(s)
- Okyaz Eminaga
- Department of Urology, Stanford University School of Medicine, Stanford
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA
| | | | - Jessie Ge
- Department of Urology, Stanford University School of Medicine, Stanford
| | - Eugene Shkolyar
- Department of Urology, Stanford University School of Medicine, Stanford
| | - Mark Laurie
- Department of Urology, Stanford University School of Medicine, Stanford
| | - Jin Long
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA
| | | | - Joseph C. Liao
- Department of Urology, Stanford University School of Medicine, Stanford
- Center for Artificial Intelligence and Medical Imaging, Stanford University School of Medicine, Stanford, CA
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Alvarez-Romero C, Martínez-García A, Sinaci AA, Gencturk M, Méndez E, Hernández-Pérez T, Liperoti R, Angioletti C, Löbe M, Ganapathy N, Deserno TM, Almada M, Costa E, Chronaki C, Cangioli G, Cornet R, Poblador-Plou B, Carmona-Pírez J, Gimeno-Miguel A, Poncel-Falcó A, Prados-Torres A, Kovacevic T, Zaric B, Bokan D, Hromis S, Djekic Malbasa J, Rapallo Fernández C, Velázquez Fernández T, Rochat J, Gaudet-Blavignac C, Lovis C, Weber P, Quintero M, Perez-Perez MM, Ashley K, Horton L, Parra Calderón CL. FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research. OPEN RESEARCH EUROPE 2022; 2:34. [PMID: 37645268 PMCID: PMC10446092 DOI: 10.12688/openreseurope.14349.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/25/2022] [Indexed: 08/31/2023]
Abstract
Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.
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Affiliation(s)
- Celia Alvarez-Romero
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain
| | - Alicia Martínez-García
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain
| | - A. Anil Sinaci
- SRDC Software Research Development and Consultancy Corporation, Ankara, 06800, Turkey
| | - Mert Gencturk
- SRDC Software Research Development and Consultancy Corporation, Ankara, 06800, Turkey
| | - Eva Méndez
- Dept. of Library & Inf Sci. Universidad Carlos III de Madrid, Getafe, 28903, Spain
| | - Tony Hernández-Pérez
- Dept. of Library & Inf Sci. Universidad Carlos III de Madrid, Getafe, 28903, Spain
| | - Rosa Liperoti
- Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Roma, 00168, Italy
| | - Carmen Angioletti
- Department of Geriatric and Orthopedic Sciences, Catholic University of Sacred Heart, Roma, 00168, Italy
| | - Matthias Löbe
- Institute for Medical Informatics (IMISE), University of Leipzig, Leipzig, 04107, Germany
| | - Nagarajan Ganapathy
- PLRI Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany
| | - Thomas M. Deserno
- PLRI Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Braunschweig, 38106, Germany
| | - Marta Almada
- Ucibio Requimte, Faculty of Pharmacy University of Porto. Porto4Ageing, Porto, 4050-313, Portugal
| | - Elisio Costa
- Ucibio Requimte, Faculty of Pharmacy University of Porto. Porto4Ageing, Porto, 4050-313, Portugal
| | | | | | - Ronald Cornet
- Amsterdam UMC, University of Amsterdam, Medical Informatics, Amsterdam Public Health, Amsterdam, 1105AZ, The Netherlands
| | - Beatriz Poblador-Plou
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Jonás Carmona-Pírez
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Antonio Gimeno-Miguel
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Antonio Poncel-Falcó
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Aragon Health Service, Zaragoza, 50009, Spain
| | - Alexandra Prados-Torres
- EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, Zaragoza, 50009, Spain
| | - Tomi Kovacevic
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Bojan Zaric
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Darijo Bokan
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Sanja Hromis
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | - Jelena Djekic Malbasa
- Medical Faculty University of Novi Sad, Novi Sad, 21000, Serbia
- Institute for Pulmonary Diseases of Vojvodina, Sremska Kamenica, 21204, Serbia
| | | | | | - Jessica Rochat
- University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland
| | | | - Christian Lovis
- University of Geneva and University hospitals of Geneva, Geneva, 1211, Switzerland
| | - Patrick Weber
- Nice Computing SA Le Mont-sur-Lausanne, Le Mont-sur-Lausanne, 1052, Switzerland
| | - Miriam Quintero
- Atos Research and Innovation - ARI. Atos IT., Madrid, 28037, Spain
- Atos Research and Innovation - ARI. Atos Spain., Madrid, 28037, Spain
| | - Manuel M. Perez-Perez
- Atos Research and Innovation - ARI. Atos IT., Madrid, 28037, Spain
- Atos Research and Innovation - ARI. Atos Spain., Madrid, 28037, Spain
| | - Kevin Ashley
- Digital Curation Centre, University of Edinburgh, Argyle House, Edinburgh, EH3 9DR, UK
| | - Laurence Horton
- Digital Curation Centre, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Carlos Luis Parra Calderón
- Computational Health Informatics Group, Institute of Biomedicine of Seville, IBiS / Virgen del Rocío University Hospital / CSIC / University of Seville, Seville, 41013, Spain
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