Nature medicine · 2021

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

СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди

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

Аннотация

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 to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Предметные рубрики MeSH

COVID-19 Electronic Health Records Humans Machine Learning Outcome Assessment, Health Care Prognosis SARS-CoV-2

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

Ошибки и ограничения

Аннотация

Предметные рубрики MeSH