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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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2
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Çubukçu HC, Topcu Dİ, Yenice S. Machine learning-based clinical decision support using laboratory data. Clin Chem Lab Med 2024; 62:793-823. [PMID: 38015744 DOI: 10.1515/cclm-2023-1037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.
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Affiliation(s)
- Hikmet Can Çubukçu
- General Directorate of Health Services, Rare Diseases Department, Turkish Ministry of Health, Ankara, Türkiye
- Hacettepe University Institute of Informatics, Ankara, Türkiye
| | - Deniz İlhan Topcu
- Health Sciences University İzmir Tepecik Education and Research Hospital, Medical Biochemistry, İzmir, Türkiye
| | - Sedef Yenice
- Florence Nightingale Hospital, Istanbul, Türkiye
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Gardiner S, Haynie C, Della Corte D. Rise of the Allotrope Simple Model: Update from 2023 Fall Allotrope Connect. Drug Discov Today 2024; 29:103944. [PMID: 38460570 DOI: 10.1016/j.drudis.2024.103944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
The Allotrope Foundation (AF) started as a group of pharmaceutical companies, instrument, and software vendors that set out to simplify the exchange of data in the laboratory. After a decade of work, they released products that have found adoption in various companies. Most recently, the Allotrope Simple Model (ASM) was developed to speed up and widen the adoption. As a result, the Foundation has recently added chemical companies and, importantly, is reworking its business model to lower the entry barrier for smaller companies. Here, we present the proceedings from the Allotrope Connect Fall 2023 conference and summarize the technical and organizational developments at the Foundation since 2020.
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Affiliation(s)
- Spencer Gardiner
- ZONTAL, Plantation, FL, USA; Deparment of Computer Science, Brigham Young University, Provo, UT, USA
| | - Christopher Haynie
- Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT, USA; ZONTAL, Plantation, FL, USA
| | - Dennis Della Corte
- ZONTAL, Plantation, FL, USA; Department of Physics and Astronomy, Brigham Young University, Provo, UT, USA.
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Marco-Ruiz L, Hernández MÁT, Ngo PD, Makhlysheva A, Svenning TO, Dyb K, Chomutare T, Llatas CF, Muñoz-Gama J, Tayefi M. A multinational study on artificial intelligence adoption: Clinical implementers' perspectives. Int J Med Inform 2024; 184:105377. [PMID: 38377725 DOI: 10.1016/j.ijmedinf.2024.105377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Despite substantial progress in AI research for healthcare, translating research achievements to AI systems in clinical settings is challenging and, in many cases, unsatisfactory. As a result, many AI investments have stalled at the prototype level, never reaching clinical settings. OBJECTIVE To improve the chances of future AI implementation projects succeeding, we analyzed the experiences of clinical AI system implementers to better understand the challenges and success factors in their implementations. METHODS Thirty-seven implementers of clinical AI from European and North and South American countries were interviewed. Semi-structured interviews were transcribed and analyzed qualitatively with the framework method, identifying the success factors and the reasons for challenges as well as documenting proposals from implementers to improve AI adoption in clinical settings. RESULTS We gathered the implementers' requirements for facilitating AI adoption in the clinical setting. The main findings include 1) the lesser importance of AI explainability in favor of proper clinical validation studies, 2) the need to actively involve clinical practitioners, and not only clinical researchers, in the inception of AI research projects, 3) the need for better information structures and processes to manage data access and the ethical approval of AI projects, 4) the need for better support for regulatory compliance and avoidance of duplications in data management approval bodies, 5) the need to increase both clinicians' and citizens' literacy as respects the benefits and limitations of AI, and 6) the need for better funding schemes to support the implementation, embedding, and validation of AI in the clinical workflow, beyond pilots. CONCLUSION Participants in the interviews are positive about the future of AI in clinical settings. At the same time, they proposenumerous measures to transfer research advancesinto implementations that will benefit healthcare personnel. Transferring AI research into benefits for healthcare workers and patients requires adjustments in regulations, data access procedures, education, funding schemes, and validation of AI systems.
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Affiliation(s)
- Luis Marco-Ruiz
- Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway.
| | | | - Phuong Dinh Ngo
- Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway
| | - Alexandra Makhlysheva
- Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway
| | - Therese Olsen Svenning
- Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway
| | - Kari Dyb
- Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway
| | - Taridzo Chomutare
- Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway
| | - Carlos Fernández Llatas
- Instituto de las Tecnologías de la Información y las Comunicaciones (ITACA), Universitat Politècnica de València (UPV), Valencia, Spain
| | - Jorge Muñoz-Gama
- Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Maryam Tayefi
- Norwegian Centre for E-Health Research, University Hospital of North Norway, Tromsø, Norway
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Tzivian L, Benis A, Rusakova A, Syundyukov E, Seidmann A, Ophir Y. International scientific communication on COVID-19 data: management pitfalls understanding. J Public Health (Oxf) 2024; 46:87-96. [PMID: 38141038 DOI: 10.1093/pubmed/fdad277] [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: 04/24/2023] [Revised: 11/23/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND During the pandemic, countries utilized various forms of statistical estimations of coronavirus disease-2019 (COVID-19) impact. Differences between databases make direct comparisons and interpretations of data in different countries a challenge. We evaluated country-specific approaches to COVID-19 data and recommended changes that would improve future international collaborations. METHODS We compared the COVID-19 reports presented on official UK (National Health System), Israeli (Department of Health), Latvian (Center for Disease Prevention and Control) and USA (Centers for Disease Control and Prevention) health authorities' websites. RESULTS Our analysis demonstrated critical differences in the ways COVID-19 statistics were made available to the general and scientific communities. Specifically, the differences in approaches were found in the presentation of the number of infected cases and tests, and percentage of positive cases, the number of severe cases, the number of vaccinated, and the number and percent of deaths. CONCLUSION Findability, Accessibility, Interoperability and Reusability principles could guide the development of essential global standards that provide a basis for communication within and outside of the scientific community.
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Affiliation(s)
- Lilian Tzivian
- Institute of Clinical and Preventive Medicine, University of Latvia, Riga LV-1586, Latvia
| | - Arriel Benis
- Digital Medical Technologies Department, Holon Institute of Technology, Holon 5810201, Israel
| | - Agnese Rusakova
- Faculty of Education, Psychology and Arts, University of Latvia, Riga LV-1586, Latvia
| | - Emil Syundyukov
- Longenesis Ltd, Riga LV-1010, Latvia
- Faculty of Computing, University of Latvia LV-1586, Riga, Latvia
| | - Abraham Seidmann
- Questrom Business School, Boston University, Boston, MA 02215, USA
- Health Analytics and Digital Health, Digital Business Institute, Boston University, Boston, MA 02215, USA
| | - Yotam Ophir
- Department of Communication, University at Buffalo, State University of New York, Buffalo, NY 14260, USA
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Niehues A, de Visser C, Hagenbeek FA, Kulkarni P, Pool R, Karu N, Kindt ASD, Singh G, Vermeiren RRJM, Boomsma DI, van Dongen J, ’t Hoen PAC, van Gool AJ. A multi-omics data analysis workflow packaged as a FAIR Digital Object. Gigascience 2024; 13:giad115. [PMID: 38217405 PMCID: PMC10787363 DOI: 10.1093/gigascience/giad115] [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: 06/13/2023] [Revised: 11/14/2023] [Accepted: 12/10/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Applying good data management and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles in research projects can help disentangle knowledge discovery, study result reproducibility, and data reuse in future studies. Based on the concepts of the original FAIR principles for research data, FAIR principles for research software were recently proposed. FAIR Digital Objects enable discovery and reuse of Research Objects, including computational workflows for both humans and machines. Practical examples can help promote the adoption of FAIR practices for computational workflows in the research community. We developed a multi-omics data analysis workflow implementing FAIR practices to share it as a FAIR Digital Object. FINDINGS We conducted a case study investigating shared patterns between multi-omics data and childhood externalizing behavior. The analysis workflow was implemented as a modular pipeline in the workflow manager Nextflow, including containers with software dependencies. We adhered to software development practices like version control, documentation, and licensing. Finally, the workflow was described with rich semantic metadata, packaged as a Research Object Crate, and shared via WorkflowHub. CONCLUSIONS Along with the packaged multi-omics data analysis workflow, we share our experiences adopting various FAIR practices and creating a FAIR Digital Object. We hope our experiences can help other researchers who develop omics data analysis workflows to turn FAIR principles into practice.
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Affiliation(s)
- Anna Niehues
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
| | - Casper de Visser
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Purva Kulkarni
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Naama Karu
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Alida S D Kindt
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Gurnoor Singh
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Robert R J M Vermeiren
- Department of Child and Adolescent Psychiatry, LUMC-Curium, Leiden University Medical Center, 2342 AK Oegstgeest, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 BT Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, 1081 BT Amsterdam, The Netherlands
- Amsterdam Reproduction & Development (AR&D) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Peter A C ’t Hoen
- Department of Medical BioSciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Alain J van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, the Netherlands
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7
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Wong WM, Tham YC, Simunovic MP, Chen FK, Luu CD, Chen H, Jin ZB, Shen RJ, Li S, Sui R, Zhao C, Yang L, Bhende M, Raman R, Sen P, Ghosh A, Poornachandra B, Sasongko MB, Arianti A, Chia V, Mangunsong CO, Manurung F, Fujinami K, Ikeda H, Woo SJ, Kim SJ, Mohd Khialdin S, Othman O, Bastion MLC, Kamalden AT, Lott PWP, Fong K, Shunmugam M, Lim A, Thapa R, Pradhan E, Rajkarnikar SP, Adhikari S, Ibañez BMBI, Koh A, Chan CMM, Fenner BJ, Tan TE, Laude A, Ngo WK, Holder GE, Su X, Chen TC, Wang NK, Kang EYC, Huang CH, Surawatsatien N, Pisuchpen P, Sujirakul T, Kumaramanickavel G, Singh M, Leroy B, Michaelides M, Cheng CY, Chen LJ, Chan HW. Rationale and protocol paper for the Asia Pacific Network for inherited eye diseases. Asia Pac J Ophthalmol (Phila) 2024; 13:100030. [PMID: 38233300 DOI: 10.1016/j.apjo.2023.100030] [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: 09/29/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 01/19/2024] Open
Abstract
PURPOSE There are major gaps in our knowledge of hereditary ocular conditions in the Asia-Pacific population, which comprises approximately 60% of the world's population. Therefore, a concerted regional effort is urgently needed to close this critical knowledge gap and apply precision medicine technology to improve the quality of lives of these patients in the Asia-Pacific region. DESIGN Multi-national, multi-center collaborative network. METHODS The Research Standing Committee of the Asia-Pacific Academy of Ophthalmology and the Asia-Pacific Society of Eye Genetics fostered this research collaboration, which brings together renowned institutions and experts for inherited eye diseases in the Asia-Pacific region. The immediate priority of the network will be inherited retinal diseases (IRDs), where there is a lack of detailed characterization of these conditions and in the number of established registries. RESULTS The network comprises 55 members from 35 centers, spanning 12 countries and regions, including Australia, China, India, Indonesia, Japan, South Korea, Malaysia, Nepal, Philippines, Singapore, Taiwan, and Thailand. The steering committee comprises ophthalmologists with experience in consortia for eye diseases in the Asia-Pacific region, leading ophthalmologists and vision scientists in the field of IRDs internationally, and ophthalmic geneticists. CONCLUSIONS The Asia Pacific Inherited Eye Disease (APIED) network aims to (1) improve genotyping capabilities and expertise to increase early and accurate genetic diagnosis of IRDs, (2) harmonise deep phenotyping practices and utilization of ontological terms, and (3) establish high-quality, multi-user, federated disease registries that will facilitate patient care, genetic counseling, and research of IRDs regionally and internationally.
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Affiliation(s)
- Wendy M Wong
- Centre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Ophthalmology, National University Hospital, National University Health System, Singapore
| | - Yih Chung Tham
- Centre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Matthew P Simunovic
- Save Sight Institute, The University of Sydney, Sydney, Australia; Retinal Unit, Sydney Eye Hospital, Sydney, Australia
| | - Fred Kuanfu Chen
- Centre for Ophthalmology and Visual Science (Lions Eye Institute), The University of Western Australia, Nedlands, Western Australia, Australia; Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
| | - Chi D Luu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia; Ophthalmology, Department of Surgery, University of Melbourne, East Melbourne, Victoria, Australia
| | - Haoyu Chen
- Joint Shantou International Eye Center, Shantou University & The Chinese University of Hong Kong, Shantou, China
| | - Zi-Bing Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China
| | - Ren-Juan Shen
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Sciences Key Laboratory, Beijing, China
| | - Shiying Li
- Department of Ophthalmology in Xiang'an Hospital of Xiamen University and Medical Center of Xiamen University, School of Medicine in Xiamen University, Eye Institute of Xiamen University, Fujian Provincial Key Laboratory of Ophthalmology and Visual Science, Fujian Engineering and Research Center of Eye Regenerative Medicine, Xiamen, Fujian, China
| | - Ruifang Sui
- Department of Ophthalmology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuai Fu Yuan, Beijing, China
| | - Chen Zhao
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Liping Yang
- Department of Ophthalmology, Peking University Third Hospital, Beijing, China; Beijing Key Laboratory of Restoration of Damaged Ocular Nerve, Peking University Third Hospital, Beijing, China
| | - Muna Bhende
- Shri Bhagwan Mahavir Vitreoretinal services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Parveen Sen
- Shri Bhagwan Mahavir Vitreoretinal services, Medical Research Foundation, Sankara Nethralaya, Chennai, India; Dr Agarwal Eye Hospital, Chandigarh, India
| | - Arkasubhra Ghosh
- GROW Lab, Narayana Nethralaya Foundation, Bangalore, Karnataka, India
| | - B Poornachandra
- Vitreo-Retina Services, Narayana Nethralaya, Bangalore, India
| | - Muhammad Bayu Sasongko
- Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada - Sardjito Eye Center, Dr. Sardjito General Hospital, Yogyakarta, Indonesia
| | - Alia Arianti
- JEC Eye Hospitals and Clinics, Jakarta, Indonesia
| | - Valen Chia
- JEC Eye Hospitals and Clinics, Jakarta, Indonesia
| | | | | | - Kaoru Fujinami
- Laboratory of Visual Physiology, Division of Vision Research, National Institute of Sensory Organs, NHO Tokyo Medical Center, Tokyo, Japan
| | - Hanako Ikeda
- Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, Japan
| | - Se Joon Woo
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sang Jin Kim
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Safinaz Mohd Khialdin
- Department of Ophthalmology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Center, Kuala Lumpur, Malaysia; UKM Specialist Children's Hospital, Kuala Lumpur, Malaysia
| | - Othmaliza Othman
- Department of Ophthalmology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Center, Kuala Lumpur, Malaysia
| | - Mae-Lynn Catherine Bastion
- Department of Ophthalmology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Center, Kuala Lumpur, Malaysia; Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Bandar Tun Razak, Kuala Lumpur, Malaysia
| | - Ain Tengku Kamalden
- UM Eye Research Centre, Department of Ophthalmology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Pooi Wah Penny Lott
- UM Eye Research Centre, Department of Ophthalmology, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | | | - Amelia Lim
- Ophthalmology, Penang Gleneagles, Malaysia
| | - Raba Thapa
- Tilganga Institute of Ophthalmology, Kathmandu, Nepal
| | - Eli Pradhan
- Tilganga Institute of Ophthalmology, Kathmandu, Nepal
| | | | | | - B Manuel Benjamin Iv Ibañez
- Makati Medical Center, Makati City, Philippines; DOH Eye Center, East Avenue Medical Center, Quezon City, Philippines
| | - Adrian Koh
- Eye & Retina Surgeons, Camden Medical Centre, Singapore, Singapore
| | - Choi Mun M Chan
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore
| | - Beau J Fenner
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore
| | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore; Ophthalmology & Visual Sciences Academic Clinical Program (EYE ACP), Duke-NUS Medical School, Singapore
| | - Augustinus Laude
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Wei Kiong Ngo
- National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore
| | - Graham E Holder
- Centre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Ophthalmology, National University Hospital, National University Health System, Singapore
| | - Xinyi Su
- Centre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Ophthalmology, National University Hospital, National University Health System, Singapore
| | - Ta-Ching Chen
- Department of Ophthalmology, National Taiwan University Hospital, Taipei, Taiwan; Center of Frontier Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Nan-Kai Wang
- Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University, New York, NY, USA; Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Eugene Yu-Chuan Kang
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chu-Hsuan Huang
- Department of Ophthalmology, Cathay General Hospital, Taipei, Taiwan
| | - Nuntachai Surawatsatien
- Center of Excellence in Retina, Department of Ophthalmology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Phattrawan Pisuchpen
- Department of Ophthalmology and Division of Academic Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Tharikarn Sujirakul
- Department of Ophthalmology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Mandeep Singh
- Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Bart Leroy
- Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium; Department of Ophthalmology, Ghent University Hospital, Ghent, Belgium
| | - Michel Michaelides
- Moorfields Eye Hospital, London, United Kingdom and UCL Institute of Ophthalmology, University College London, London, United Kingdom
| | - Ching-Yu Cheng
- Centre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Li Jia Chen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Hwei Wuen Chan
- Centre for Innovation & Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Ophthalmology, National University Hospital, National University Health System, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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Bernabé CH, Queralt-Rosinach N, Silva Souza VE, Bonino da Silva Santos LO, Mons B, Jacobsen A, Roos M. The use of foundational ontologies in biomedical research. J Biomed Semantics 2023; 14:21. [PMID: 38082345 PMCID: PMC10712036 DOI: 10.1186/s13326-023-00300-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The FAIR principles recommend the use of controlled vocabularies, such as ontologies, to define data and metadata concepts. Ontologies are currently modelled following different approaches, sometimes describing conflicting definitions of the same concepts, which can affect interoperability. To cope with that, prior literature suggests organising ontologies in levels, where domain specific (low-level) ontologies are grounded in domain independent high-level ontologies (i.e., foundational ontologies). In this level-based organisation, foundational ontologies work as translators of intended meaning, thus improving interoperability. Despite their considerable acceptance in biomedical research, there are very few studies testing foundational ontologies. This paper describes a systematic literature mapping that was conducted to understand how foundational ontologies are used in biomedical research and to find empirical evidence supporting their claimed (dis)advantages. RESULTS From a set of 79 selected papers, we identified that foundational ontologies are used for several purposes: ontology construction, repair, mapping, and ontology-based data analysis. Foundational ontologies are claimed to improve interoperability, enhance reasoning, speed up ontology development and facilitate maintainability. The complexity of using foundational ontologies is the most commonly cited downside. Despite being used for several purposes, there were hardly any experiments (1 paper) testing the claims for or against the use of foundational ontologies. In the subset of 49 papers that describe the development of an ontology, it was observed a low adherence to ontology construction (16 papers) and ontology evaluation formal methods (4 papers). CONCLUSION Our findings have two main implications. First, the lack of empirical evidence about the use of foundational ontologies indicates a need for evaluating the use of such artefacts in biomedical research. Second, the low adherence to formal methods illustrates how the field could benefit from a more systematic approach when dealing with the development and evaluation of ontologies. The understanding of how foundational ontologies are used in the biomedical field can drive future research towards the improvement of ontologies and, consequently, data FAIRness. The adoption of formal methods can impact the quality and sustainability of ontologies, and reusing these methods from other fields is encouraged.
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Affiliation(s)
- César H Bernabé
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
| | | | | | - Luiz Olavo Bonino da Silva Santos
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- University of Twente, Enschede, The Netherlands
| | - Barend Mons
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Annika Jacobsen
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Marco Roos
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
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9
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Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler C, Waltemath D, Zoch M, Sedlmayr M. Ten Topics to Get Started in Medical Informatics Research. J Med Internet Res 2023; 25:e45948. [PMID: 37486754 PMCID: PMC10407648 DOI: 10.2196/45948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 07/25/2023] Open
Abstract
The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data.
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Affiliation(s)
- Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
| | - Najia Ahmadi
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kai Fitzer
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
| | - Sophia Grummt
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Kilian-Ludwig Heine
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ian-C Jung
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center, Goettingen, Germany
| | - Andreas Kühn
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Yuan Peng
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Ines Reinecke
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Tobias Schmidt
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Paul Schmücker
- Institute for Medical Informatics, University of Applied Sciences Mannheim, Mannheim, Germany
| | - Christina Schüttler
- Central Biobank Erlangen, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Dagmar Waltemath
- Core Unit Data Integration Center, University Medicine Greifswald, Greifswald, Germany
- Department of Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Zoch
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Dresden, Germany
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10
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de Kok JWTM, de la Hoz MÁA, de Jong Y, Brokke V, Elbers PWG, Thoral P, Castillejo A, Trenor T, Castellano JM, Bronchalo AE, Merz TM, Faltys M, van der Horst ICC, Xu M, Celi LA, van Bussel BCT, Borrat X. A guide to sharing open healthcare data under the General Data Protection Regulation. Sci Data 2023; 10:404. [PMID: 37355751 PMCID: PMC10290652 DOI: 10.1038/s41597-023-02256-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 05/17/2023] [Indexed: 06/26/2023] Open
Abstract
Sharing healthcare data is increasingly essential for developing data-driven improvements in patient care at the Intensive Care Unit (ICU). However, it is also very challenging under the strict privacy legislation of the European Union (EU). Therefore, we explored four successful open ICU healthcare databases to determine how open healthcare data can be shared appropriately in the EU. A questionnaire was constructed based on the Delphi method. Then, follow-up questions were discussed with experts from the four databases. These experts encountered similar challenges and regarded ethical and legal aspects to be the most challenging. Based on the approaches of the databases, expert opinion, and literature research, we outline four distinct approaches to openly sharing healthcare data, each with varying implications regarding data security, ease of use, sustainability, and implementability. Ultimately, we formulate seven recommendations for sharing open healthcare data to guide future initiatives in sharing open healthcare data to improve patient care and advance healthcare.
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Affiliation(s)
- Jip W T M de Kok
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Miguel Á Armengol de la Hoz
- Big Data Department, PMC, Fundacion Progreso y Salud (FPS), Regional Ministry of Health of Andalucia, Seville, Andalucia, Spain
| | | | | | - Paul W G Elbers
- Center for Critical Care Computational Intelligence (C4I), Department of Intensive Care Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick Thoral
- Center for Critical Care Computational Intelligence (C4I), Department of Intensive Care Medicine, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | - Jose M Castellano
- Fundación de Investigación HM Hospitales, Grupo HM Hospitales, Madrid, Spain
| | - Alberto E Bronchalo
- Fundación de Investigación HM Hospitales, Grupo HM Hospitales, Madrid, Spain
| | - Tobias M Merz
- Cardiovascular Intensive Care Unit, Auckland City Hospital, Auckland, New Zealand
| | - Martin Faltys
- Department of Intensive Care Medicine, University Hospital, University of Bern, Bern, Switzerland
| | - Iwan C C van der Horst
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
| | - Minnan Xu
- Philips Research North America, Cambridge, MA, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
- Department of Biostatistics Harvard T.H, Chan School of Public Health, Boston, Massachusetts, USA
| | - Bas C T van Bussel
- Department of Intensive Care Medicine, Maastricht University Medical Centre+, Maastricht, the Netherlands.
- Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.
- Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands.
| | - Xavier Borrat
- Department of Biostatistics Harvard T.H, Chan School of Public Health, Boston, Massachusetts, USA.
- Anaesthesiology and Critical Care Department, Hospital Clinic de Barcelona, Barcelona, Spain.
- Medical Informatics Department, Hospital Clinic de Barcelona, Barcelona, Spain.
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11
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Diniz JM, Vasconcelos H, Souza J, Rb-Silva R, Ameijeiras-Rodriguez C, Freitas A. Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review. JMIR Res Protoc 2023; 12:e45823. [PMID: 37335606 DOI: 10.2196/45823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications. OBJECTIVE The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures. METHODS We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)-based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported. RESULTS The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings. CONCLUSIONS We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients' privacy and future research. TRIAL REGISTRATION PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/45823.
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Affiliation(s)
- José Miguel Diniz
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
- PhD Program in Health Data Science, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Henrique Vasconcelos
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Júlio Souza
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Rita Rb-Silva
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Carolina Ameijeiras-Rodriguez
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Alberto Freitas
- CINTESIS-Centre for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, Portugal
- MEDCIDS-Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal
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12
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Sakor A, Jozashoori S, Niazmand E, Rivas A, Bougiatiotis K, Aisopos F, Iglesias E, Rohde PD, Padiya T, Krithara A, Paliouras G, Vidal ME. Knowledge4COVID-19: A semantic-based approach for constructing a COVID-19 related knowledge graph from various sources and analyzing treatments' toxicities. WEB SEMANTICS (ONLINE) 2023; 75:100760. [PMID: 36268112 PMCID: PMC9558693 DOI: 10.1016/j.websem.2022.100760] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 09/08/2022] [Accepted: 10/05/2022] [Indexed: 05/20/2023]
Abstract
In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources of knowledge to discover adverse drug effects caused by drug-drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, we focus on constructing the Knowledge4COVID-19 knowledge graph (KG) from the declarative definition of mapping rules using the RDF Mapping Language. Since valuable information about drug treatments, drug-drug interactions, and side effects is present in textual descriptions in scientific databases (e.g., DrugBank) or in scientific literature (e.g., the CORD-19, the Covid-19 Open Research Dataset), the Knowledge4COVID-19 framework implements Natural Language Processing. The Knowledge4COVID-19 framework extracts relevant entities and predicates that enable the fine-grained description of COVID-19 treatments and the potential adverse events that may occur when these treatments are combined with treatments of common comorbidities, e.g., hypertension, diabetes, or asthma. Moreover, on top of the KG, several techniques for the discovery and prediction of interactions and potential adverse effects of drugs have been developed with the aim of suggesting more accurate treatments for treating the virus. We provide services to traverse the KG and visualize the effects that a group of drugs may have on a treatment outcome. Knowledge4COVID-19 was part of the Pan-European hackathon#EUvsVirus in April 2020 and is publicly available as a resource through a GitHub repository and a DOI.
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Affiliation(s)
- Ahmad Sakor
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
| | - Samaneh Jozashoori
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
| | - Emetis Niazmand
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
| | - Ariam Rivas
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
| | - Konstantinos Bougiatiotis
- Institute of Informatics & Telecommunications, NCSR Demokritos, Patr. Grigoriou & Neapoleos Str, Ag. Paraskevi, Athens, Greece
- Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiou 30, Athens, Greece
| | - Fotis Aisopos
- Institute of Informatics & Telecommunications, NCSR Demokritos, Patr. Grigoriou & Neapoleos Str, Ag. Paraskevi, Athens, Greece
| | - Enrique Iglesias
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
| | - Philipp D Rohde
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
| | - Trupti Padiya
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
| | - Anastasia Krithara
- Institute of Informatics & Telecommunications, NCSR Demokritos, Patr. Grigoriou & Neapoleos Str, Ag. Paraskevi, Athens, Greece
| | - Georgios Paliouras
- Institute of Informatics & Telecommunications, NCSR Demokritos, Patr. Grigoriou & Neapoleos Str, Ag. Paraskevi, Athens, Greece
| | - Maria-Esther Vidal
- TIB Leibniz Information Centre for Science and Technology, Welfengarten 1 B, Hannover, Germany
- L3S Research Center, University of Hannover, Appelstraße 9a, Hannover, Germany
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