Federated learning for predicting clinical outcomes in patients with COVID-19.
- Ittai Dayan ,
- Holger R. Roth ,
- Aoxiao Zhong ,
- Ahmed Harouni ,
- Amilcare Gentili ,
- Anas Z. Abidin ,
- Andrew Liu ,
- Anthony Beardsworth Costa ,
- Bradford J. Wood ,
- Chien-Sung Tsai ,
- Chih-Hung Wang ,
- Chun-Nan Hsu ,
- C. K. Lee ,
- Peiying Ruan ,
- Daguang Xu ,
- Dufan Wu ,
- Eddie Huang ,
- Felipe Campos Kitamura ,
- Griffin Lacey ,
- Gustavo César de Antônio Corradi ,
- Gustavo Nino ,
- Hao-Hsin Shin ,
- Hirofumi Obinata ,
- Hui Ren ,
- Jason C. Crane ,
- Jesse Tetreault ,
- Jiahui Guan ,
- John W. Garrett ,
- Joshua D. Kaggie ,
- Jung Gil Park ,
- Keith Dreyer ,
- Krishna Juluru ,
- Kristopher Kersten ,
- Marcio Aloisio Bezerra Cavalcanti Rockenbach ,
- Marius George Linguraru ,
- Masoom A. Haider ,
- Meena AbdelMaseeh ,
- Nicola Rieke ,
- Pablo F. Damasceno ,
- Pedro Mario Cruz e Silva ,
- Pochuan Wang ,
- Sheng Xu ,
- Shuichi Kawano ,
- Sira Sriswasdi ,
- Soo Young Park ,
- Thomas M. Grist ,
- Varun Buch ,
- Watsamon Jantarabenjakul ,
- Weichung Wang ,
- Won Young Tak ,
- Xiang Li ,
- Xihong Lin ,
- Young Joon Kwon ,
- Abood Quraini ,
- Andrew Feng ,
- Andrew N. Priest ,
- Baris Turkbey ,
- Benjamin Glicksberg ,
- Bernardo Bizzo ,
- Byung Seok Kim ,
- Carlos Tor-Díez ,
- Chia-Cheng Lee ,
- Chia-Jung Hsu ,
- Chin Lin ,
- Chiu-Ling Lai ,
- Christopher P. Hess ,
- Colin Compas ,
- Deepeksha Bhatia ,
- Eric K. Oermann ,
- Evan Leibovitz ,
- Hisashi Sasaki ,
- Hitoshi Mori ,
- Isaac Yang ,
- Jae Ho Sohn ,
- Krishna Nand Keshava Murthy ,
- Li-Chen Fu ,
- Matheus R. F. Mendonça ,
- Mike Fralick ,
- Min Kyu Kang ,
- Mohammad Adil ,
- Natalie Gangai ,
- Peerapon Vateekul ,
- Pierre Elnajjar ,
- Sarah Hickman ,
- Sharmila Majumdar ,
- Shelley L. McLeod ,
- Sheridan Reed ,
- Stefan Gräf ,
- Stephanie Harmon ,
- Tatsuya Kodama ,
- Thanyawee Puthanakit ,
- Tony Mazzulli ,
- Vitor Lima de Lavor ,
- Yothin Rakvongthai ,
- Yu Rim Lee ,
- Yuhong Wen ,
- Fiona J. Gilbert ,
- Mona G. Flores ,
- Quanzheng Li
Nature Medicine | , Vol 27(10): pp. 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 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. Federated learning, a method for training artificial intelligence algorithms that protects data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes across the globe.