Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.

JCO Clinical Cancer Informatics
Fadila ZerkaPhilippe Lambin

Abstract

Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in par...Continue Reading

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Citations

Dec 4, 2020·Computers in Biology and Medicine·Chandra Thapa, Seyit Camtepe
Aug 18, 2020·Journal of the American Medical Informatics Association : JAMIA·Melissa A HaendelUNKNOWN N3C Consortium
Mar 20, 2021·Médecine sciences : M/S·Marcel Goldberg, Marie Zins
Apr 14, 2021·European Journal of Nuclear Medicine and Molecular Imaging·Margarita KirienkoArturo Chiti
Jul 27, 2021·Medicinal Research Reviews·Julien GuiotRalph T H Leijenaar
Aug 8, 2021·Computers in Biology and Medicine·Fadila ZerkaPhilippe Lambin
Sep 4, 2021·Yearbook of Medical Informatics·Scott P McGrathNicholas P Tatonetti

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