TY - JOUR
T1 - SCOR
T2 - A secure international informatics infrastructure to investigate COVID-19
AU - Raisaro, J. L.
AU - Marino, Francesco
AU - Troncoso-Pastoriza, Juan
AU - Beau-Lejdstrom, Raphaelle
AU - Bellazzi, Riccardo
AU - Murphy, Robert
AU - Bernstam, Elmer V.
AU - Wang, Henry
AU - Bucalo, Mauro
AU - Chen, Yong
AU - Gottlieb, Assaf
AU - Harmanci, Arif
AU - Kim, Miran
AU - Kim, Yejin
AU - Klann, Jeffrey
AU - Klersy, Catherine
AU - Malin, Bradley A.
AU - Meán, Marie
AU - Prasser, Fabian
AU - Scudeller, Luigia
AU - Torkamani, Ali
AU - Vaucher, Julien
AU - Puppala, Mamta
AU - Wong, Stephen T.C.
AU - Frenkel-Morgenstern, Milana
AU - Xu, Hua
AU - Musa, Baba Maiyaki
AU - Habib, Abdulrazaq G.
AU - Cohen, Trevor
AU - Wilcox, Adam
AU - Salihu, Hamisu M.
AU - Sofia, Heidi
AU - Jiang, Xiaoqian
AU - Hubaux, J. P.
N1 - Publisher Copyright:
© 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.
PY - 2020/11/1
Y1 - 2020/11/1
N2 - 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.
AB - 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.
KW - COVID-19
KW - federated learning
KW - healthcare privacy
KW - international consortium
KW - secure data analysis
UR - http://www.scopus.com/inward/record.url?scp=85089117172&partnerID=8YFLogxK
U2 - 10.1093/jamia/ocaa172
DO - 10.1093/jamia/ocaa172
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C2 - 32918447
AN - SCOPUS:85089117172
SN - 1067-5027
VL - 27
SP - 1721
EP - 1726
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 11
ER -