Dayan IRoth H.RZhong AHarouni AGentili AAbidin A.ZLiu ACosta A.BWood B.JTsai C.-SWang C.-HHsu C.-NLee C.KRuan PXu DWu DHuang EKitamura F.CLacey Gde Antônio Corradi G.CNino GShin H.-HObinata HRen HCrane J.CTetreault JGuan JGarrett J.WKaggie J.DPark J.GDreyer KJuluru KKersten KRockenbach M.A.B.CLinguraru M.GHaider M.AAbdelMaseeh MRieke NDamasceno P.Fe Silva P.M.CWang PXu SKawano SSriswasdi SPark S.YGrist T.MBuch VJantarabenjakul WWEICHUNG WANGTak W.YLi XLin XKwon Y.JQuraini AFeng APriest A.NTurkbey BGlicksberg BBizzo BKim B.STor-Díez CLee C.-CHsu C.-JLin CLai C.-LHess C.PCompas CBhatia DOermann E.KLeibovitz ESasaki HMori HYang ISohn J.HMurthy K.N.KLI-CHEN FUde Mendonça M.R.FFralick MKang M.KAdil MGangai NVateekul PElnajjar PHickman SMajumdar SMcLeod S.LReed SGräf SHarmon SKodama TPuthanakit TMazzulli Tde Lavor V.LRakvongthai YLee Y.RWen YGilbert F.JFlores M.GLi Q.2022-04-252022-04-25202110788956https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115148251&doi=10.1038%2fs41591-021-01506-3&partnerID=40&md5=170b35467280264a48c10b40677c61eahttps://scholars.lib.ntu.edu.tw/handle/123456789/606448Federated 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. ? 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.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. © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.C reactive proteinoxygenadultArticleartificial ventilationclinical outcomecohort analysiscomparative studycontrolled studycoronavirus disease 2019electronic medical recordemergency wardfemalehealth care systemhumanmaleoxygen consumptionoxygen therapypredictionsensitivity and specificitythorax radiographyelectronic health recordisolation and purificationmachine learningpathophysiologyprognosistherapyvirologyCOVID-19Electronic Health RecordsHumansMachine LearningOutcome Assessment, Health CarePrognosisSARS-CoV-2[SDGs]SDG3C reactive protein; oxygen; adult; Article; artificial ventilation; clinical outcome; cohort analysis; comparative study; controlled study; coronavirus disease 2019; electronic medical record; emergency ward; female; health care system; human; male; oxygen consumption; oxygen therapy; prediction; sensitivity and specificity; thorax radiography; electronic health record; isolation and purification; machine learning; pathophysiology; prognosis; therapy; virology; COVID-19; Electronic Health Records; Humans; Machine Learning; Outcome Assessment, Health Care; Prognosis; SARS-CoV-2journal article10.1038/s41591-021-01506-3345266992-s2.0-85115148251