SCOR: A secure international informatics infrastructure to investigate COVID-19
Open accessAbstract
Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, and disease progression patterns. Despite the enormous efforts of many large data consortium initiatives, scientific community still lacks a secure and privacy-preserving infrastructure to support auditable data sharing and facilitate automated and legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate the latest progress in modern security and federated machine learning algorithms, which are poised to offer solutions. An international group of passionate researchers came together with a joint mission to solve the problem with our finest models and tools. The SCOR Consortium has developed a ready-to-deploy secure infrastructure using world-class privacy and security technologies to reconcile the privacy/utility conflicts. We hope our effort will make a change and accelerate research in future pandemics with broad and diverse samples on an international scale.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Federated learning has shown promise in recent medical imaging applications, including COVID-19 analysis.
“FL has already shown promise in recent medical imaging applications40–43, including in COVID-19 analysis8,44,45.”
From Federated learning for predicting clinical outcomes in patients with COVID-19 (2021) · cites this paper as [44]
This is the first study to evaluate the efficacy of applying federated learning to predict mortality for COVID-19 using clinical data from EHRs.
“While it has been proposed, to our knowledge there are no published studies that implement and assess the utility of federated learning to predict COVID-19 outcomes from Electronic Health Record (EHR) data.[17]”
From Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19 (2020) · cites this paper as [17]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
2 retracted
RETRACTED: Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19
Mehra, M. R., Desai, S. S., Kuy, S., Henry, T. D., & Patel, A. N. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2007621 · Cited by 1,428 · see citing works
RetractedMehra, M. R., Desai, S. S., Ruschitzka, F. T., & Patel, A. N. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31180-6 · Cited by 1,201 · see citing works
RetractedRichardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., Davidson, K. W., and the Northwell COVID-19 Research Consortium, Barnaby, D. P., Becker, L. B., Chelico, J. D., et al. (2020) · JAMA · doi:10.1001/jama.2020.6775 · Cited by 9,254 · see citing works
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571 · see citing works
Incidence of thrombotic complications in critically ill ICU patients with COVID-19
Klok, F. A., Kruip, M. J. H. A., van der Meer, N. J. M., Arbous, M. S., Gommers, D. A. M. P. J., Kant, K. M., Kaptein, F. H. J., van Paassen, J., Stals, M. A. M., Huisman, M. V., et al. (2020) · Thrombosis Research · doi:10.1016/j.thromres.2020.04.013 · Cited by 5,028 · see citing works
Helms, J., Tacquard, C. A., Séverac, F., Léonard-Lorant, I., Ohana, M., Delabranche, X., Merdji, H., Clère-Jehl, R., Schenck, M., Gandet, F. F., et al. (2020) · Intensive Care Medicine · doi:10.1007/s00134-020-06062-x · Cited by 2,818 · see citing works
Prevalence of venous thromboembolism in patients with severe novel coronavirus pneumonia
Cui, S., Chen, S., Li, X., Liu, S., & Wang, F. (2020) · Journal of Thrombosis and Haemostasis · doi:10.1111/jth.14830 · Cited by 1,805 · see citing works
COVID‐19 patients' clinical characteristics, discharge rate, and fatality rate of meta‐analysis
Li, L., Huang, T., Wang, Y., Wang, Z., Liang, Y., Huang, T., Zhang, H., Sun, W., & Wang, Y. (2020) · Journal of Medical Virology · doi:10.1002/jmv.25757 · Cited by 1,492 · see citing works
Doubly robust estimation of causal effects
Funk, M. J., Westreich, D., Wiesen, C., Stürmer, T., Brookhart, M. A., & Davidian, M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq439 · Cited by 1,158 · see citing works
Encyclopedia of Cryptography and Security
Lange, T., van Tilborg, H. C. A., & Jajodia, S. (2011) · Springer eBooks · doi:10.1007/978-1-4419-5906-5 · Cited by 700 · see citing works
Dwork, C. (2011) · Springer eBooks · doi:10.1007/978-1-4419-5906-5_752 · Cited by 341 · see citing works
COVID-19 and the Need for a National Health Information Technology Infrastructure
Sittig, D. F., & Singh, H. (2020) · JAMA · doi:10.1001/jama.2020.7239 · Cited by 90 · see citing works
How sick will the coronavirus make you? The answer may be in your genes
Kaiser, J. (2020) · Science · doi:10.1126/science.abb9192 · Cited by 54 · see citing works
Fool's Gold: An Illustrated Critique of Differential PrivacyPreprint
Bambauer, J. R. Y., Muralidhar, K., & Sarathy, R. (2013) · SSRN Electronic Journal · Cited by 45 · see citing works
Identifying inference attacks against healthcare data repositories.
Vaidya, J. S., Shafiq, B., Jiang, X., & Lucila Ohno-machado, A. (2013) · PubMed Central · Cited by 23 · see citing works
Self-citationSecure Multiparty Computing Protocol
Shaikh, Z., & Garg, P. (2013) · Advances in e-business research series · doi:10.4018/978-1-4666-4209-6.ch012 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [5]Mehra MR, Desai SS, Kuy S, et al Retraction: cardiovascular disease, drug therapy, and mortality in COVID-19. N Engl J Med 2020; 382 (25): e102. 32356626 PMC7206931
- [16]Gentry C. Fully homomorphic encryption using ideal lattices. In: proceedings of the forty-first annual ACM symposium on Theory of computing. New York, NY: Association for Computing Machinery; 2009: 169– 78.
Cited by
Sorted by citations
Federated learning for predicting clinical outcomes in patients with COVID-19
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01506-3 · Cited by 842 · see citing works
“FL has already shown promise in recent medical imaging applications 40–43 , including in COVID-19 analysis 8,44,45 .”
BackgroundEnd-to-end privacy preserving deep learning on multi-institutional medical imaging
Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00337-8 · Cited by 513 · see citing works
Rauniyar, A., Hagos, D. H., Jha, D., Håkegård, J. E., Bagci, U., Rawat, D. B., & Vlassov, V. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3329061 · Cited by 315 · see citing works
Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J. K. D., Wanyan, T., et al. (2020) · doi:10.1101/2020.08.11.20172809 · Cited by 253 · see citing works
- Authors
- Raisaro, J. L., Marino, F., Troncoso-Pastoriza, J., Beau-Lejdstrom, R., Bellazzi, R., Murphy, R., Bernstam, E. V., Wang, H., Bucalo, M., Chen, Y., Gottlieb, A., Harmanci, A., Kim, M., Kim, Y., Klann, J., Klersy, C., Malin, B. A., Méan, M., Prasser, F., Scudeller, L., Torkamani, A., Vaucher, J., Puppala, M., Wong, S. T. C., Frenkel-Morgenstern, M., Xu, H., Musa, B. M., Habib, A. G., Cohen, T., Wilcox, A., Salihu, H. M., Sofia, H., Jiang, X., & Hubaux, J. P.
- Affiliations
- Centre Hospitalier Universitaire Vaudois (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- Institute of Global Health, University of Geneva, Geneva, Switzerland
- University of Pavia (IT)
- The University of Texas Health Science Center at Houston (US)
- BIOMERIS srl, Pavia, Italy
- University of Pennsylvania (US)
- Massachusetts General Hospital (US)
- Policlinico San Matteo Fondazione (IT)
- Vanderbilt University Medical Center (US)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
- Scripps Research Institute (US)
- Houston Methodist (US)
- Cancer Genomics and BioComputing of Complex Diseases laboratory, Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
- Bayero University Kano (NG)
- University of Washington (US)
- Baylor College of Medicine (US)
- National Institutes of Health (US)
- Corresponding author
- Jiang, Xiaoqian
- ORCID
- Raisaro, J L
- Marino, Francesco
- Troncoso-Pastoriza, Juan
- Bellazzi, Riccardo
- Murphy, Robert
- Bernstam, Elmer V
- Wang, Henry
- Bucalo, Mauro
- Chen, Yong
- Gottlieb, Assaf
- Harmanci, Arif
- Kim, Miran
- Kim, Yejin
- Klann, Jeffrey
- Klersy, Catherine
- Malin, Bradley A
- Méan, Marie
- Prasser, Fabian
- Scudeller, Luigia
- Torkamani, Ali
- Vaucher, Julien
- Puppala, Mamta
- Wong, Stephen T C
- Xu, Hua
- Musa, Baba Maiyaki
- Habib, Abdulrazaq G
- Cohen, Trevor
- Wilcox, Adam
- Salihu, Hamisu M
- Sofia, Heidi
- Jiang, Xiaoqian
- Hubaux, J P
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-07-10
- Volume
- 27
- Issue
- 11
- Pages
- 1721-1726
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC7454652
- OpenAlex
- W3041275674
- Cited by
- 40
- References
- 16
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 10 · 2021: 15 · 2020: 3
- FWCI
- 3.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- SERVAL (Université de Lausanne) (publishedVersion, cc-by-nc)
- IRIS (submittedVersion, cc-by-nc)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
- Funders
- COVID-19 Data Science Institute (DSI), Bar-Ilan University (247017)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › COVID-19 Digital Contact Tracing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Keywords
- COVID-19 pandemic, federated learning, privacy-preserving data sharing, healthcare data privacy, privacy-utility trade-off, pandemic research
- MeSH terms
- Machine Learning, COVID-19, Humans, Pneumonia, Viral, Computer Security, Coronavirus Infections, Privacy, Information Dissemination, Biomedical Research, Internationality, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
