Federated learning for predicting clinical outcomes in patients with COVID-19

Autor: Jiahui Guan, Krishna Juluru, Yothin Rakvongthai, Benjamin S. Glicksberg, Watsamon Jantarabenjakul, Li-Chen Fu, Mike Fralick, Anthony Costa, Quanzheng Li, Andrew Feng, Eric K. Oermann, Joshua D. Kaggie, Xihong Lin, Pedro Mário Cruz e Silva, Deepeksha Bhatia, Byung Seok Kim, Hitoshi Mori, Pablo F. Damasceno, Peiying Ruan, Yuhong Wen, Hao-Hsin Shin, Amilcare Gentili, Weichung Wang, Chiu-Ling Lai, Jason C. Crane, Andrew N. Priest, Soo-Young Park, Peerapon Vateekul, Matheus Ribeiro Furtado de Mendonça, Gustavo César de Antônio Corradi, Griffin Lacey, Meena AbdelMaseeh, Yu Rim Lee, Tatsuya Kodama, Pierre Elnajjar, Krishna Nand Keshava Murthy, Xiang Li, Evan Leibovitz, Vitor Lavor, Christopher P. Hess, Colin B. Compas, Stefan Gräf, Masoom A. Haider, Daguang Xu, Nicola Rieke, Thanyawee Puthanakit, Sarah E Hickman, Hui Ren, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Jung Gil Park, Jesse Tetreault, Hisashi Sasaki, Min Kyu Kang, Won Young Tak, Chun-Nan Hsu, Fiona J. Gilbert, Chin Lin, Varun Buch, Felipe Kitamura, Tony Mazzulli, Eddie Huang, Abood Quraini, Shelley McLeod, Young Joon Kwon, Gustavo Nino, Dufan Wu, Chien-Sung Tsai, Mona Flores, Baris Turkbey, Sira Sriswasdi, Pochuan Wang, Mohammad Adil, Aoxiao Zhong, Chih-Hung Wang, Sheng Xu, C. K. Lee, Isaac Yang, Marius George Linguraru, Holger R. Roth, Chia-Jung Hsu, Anas Z. Abidin, Thomas M. Grist, Hirofumi Obinata, Sheridan Reed, Andrew Liu, Ahmed Harouni, Natalie Gangai, Ittai Dayan, Kristopher Kersten, Stephanie Harmon, Jae Ho Sohn, John Garrett, Bradford J. Wood, Sharmila Majumdar, Bernardo Bizzo, Shuichi Kawano, Keith J. Dreyer, Carlos Tor-Díez, Chia-Cheng Lee

Publikováno v: Nature Medicine. 27:1735-1743

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c204df8c07fd165584f2de19871d49b1
https://doi.org/10.1038/s41591-021-01506-3

Federated Learning used for predicting outcomes in SARS-COV-2 patients

Autor: Matheus Ribeiro Furtado de Mendonça, Evan Leibovitz, Kristopher Kersten, Mona Flores, John Garrett, Baris Turkbey, Pablo F. Damasceno, Masoom A. Haider, Fred Kwon, Soo-Young Park, Chun-Nan Hsu, Keith J. Dreyer, Chien-Sung Tsai, Tatsuya Kodama, Daguang Xu, Min Kyu Kang, Tony Mazzulli, Andrew Feng, C. K. Lee, Isaac Yang, Deepi Bhatia, Marius George Linguraru, Byung Seok Kim, Aoxiao Zhong, Mohammad Adil, Pochuan Wang, Sheridan Reed, Peerapon Vateekul, Anas Z. Abidin, Sira Sriswa, J. D. Kaggie, Chia-Cheng Lee, Carlos Tor-Díez, Krishna Juluru, Xiang Li, Colin B. Compas, Xihong Lin, Jiahui Guan, Pierre Elnajjar, Yuhong Wen, Jung Gil Park, Hao-Hsin Shin, Amilcare Gentili, Weichung Wang, Colleen Ruan, Hui Ren, Hisashi Sasaki, Hitoshi Mori, Holger R. Roth, Felipe Kitamura, Chiu-Ling Lai, Jason C. Crane, Thomas M. Grist, Bradford J. Wood, Bernardo Bizzo, Dufan Wu, Jesse Tetreault, Andrew N. Priest, Mike Fralick, Anthony Costa, Andrew Liu, Benjamin S. Glicksberg, Griffin Lacey, Meena Abdelmaseeh, Thanyawee Puthanakit, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Shelley McLeod, Pedro Mário Cruz e Silva, Chih-Hung Wang, Chia-Jung Hsu, Sarah E Hickman, Won Young Tak, Quanzheng Li, Yothin Rakvongthai, Watsamon Jantarabenjakul, Li-Chen Fu, Gustavo César de Antônio Corradi, Eric K. Oermann, Nicola Rieke, Varun Buch, Abood Quraini, Shuichi Kawano, Natalie Gangai, Yu Rim Lee, Krishna Nand Keshava Murthy, Christopher P. Hess, Stefan Gräf, Ittai Dayan, Stephanie Harmon, Jae Ho Sohn, Eddie Huang, Ahmed Harouni, Vitor de Lima Lavor, Sharmila Majumdar, Sheng Xu, Hirofumi Obinata, Fiona J. Gilbert, Chin Lin

Publikováno v: Research Square
article-version (status) pre
article-version (number) 1
Nat Med

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a41590ae4e4469b7160c678aeaab558
http://europepmc.org/articles/PMC7805458