Federated learning for predicting clinical outcomes in patients with COVID-19.
Federated learning for predicting clinical outcomes in patients with COVID-19.
复制标题
DOI：
10.1038/s41591-021-01506-3
复制
发表时间：
2021-10
期刊：
影响因子：
82.9
通讯作者：
Li, Quanzheng
中科院分区：
文献类型：
--
作者：
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 Antonio Corradi, Gustavo Cesar;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.;Silva, Pedro Mario Cruz E.;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-Diez, 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;Furtado de Mendonca, Matheus Ribeiro;Fralick, Mike;Kang, Min Kyu;Adil, Mohammad;Gangai, Natalie;Vateekul, Peerapon;Elnajjar, Pierre;Hickman, Sarah;Majumdar, Sharmila;McLeod, Shelley L.;Reed, Sheridan;Graf, 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
联合学习(FL)是一种用来自多个来源的数据训练人工智能(AI)模型的方法，同时保持数据的匿名性，从而消除了数据共享的许多障碍。在这里，我们使用来自全球20个研究所的数据来训练FL模型，称为“Exam”(EMR CXR AI Model)，该模型使用生命体征、实验室数据和胸部X光的输入来预测有症状的新冠肺炎患者未来的氧气需求。从最初提交给急诊室的时间开始，Exam在24小时和72小时预测结果的平均曲线下面积(AUC)超过0.92，它使所有参与站点的平均AUC提高了16%，与使用该站点数据在单个站点培训的模型相比，泛化能力平均提高了38%。在最大的独立试验点预测未来24小时机械通气(MV)治疗或死亡的灵敏度为0.950，特异度为0.882。在这项研究中，FL在没有数据交换的情况下促进了快速的数据科学协作，并生成了一个跨异质、未协调的数据集通用的模型，用于预测新冠肺炎患者的临床结果，为FL在医疗保健中的更广泛使用奠定了基础。
Federated learning (FL) is a method for training artificial intelligence (AI) models with data from multiple sources while maintaining the anonymity of the data, thus removing many barriers to data sharing. Here we use data from 20 institutes across the globe to train a FL model, called “EXAM” (EMR CXR AI Model), that predicts future oxygen requirements of symptomatic COVID-19 patients using inputs of vital signs, laboratory data, and chest X-rays. EXAM achieved an average area under the curve (AUC) greater than 0.92 for predicting outcomes at 24 and 72h from the time of initial presentation to the ER, and it provided a 16% improvement of the average AUC measured across all participating sites, and an average increase in generalizability of 38% when compared to models trained at a single site using that site’s data. For predicting mechanical ventilation (MV) treatment or death at 24 h in the future at the largest independent test site, EXAM achieved a sensitivity of 0.950 and a 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 predicting clinical outcomes in COVID-19 patients, setting the stage for broader use of FL in healthcare.
登录
查看更多内容
通过电子病历预测COVID-19死亡率。
影响因子：
15.2
作者：
Estiri H;Strasser ZH;Klann JG;Naseri P;Wagholikar KB;Murphy SN
通讯作者：
Murphy SN
影响因子：
28.2
作者：
Galloway, James B.;Norton, Sam;Cantle, Fleur
通讯作者：
Cantle, Fleur
影响因子：
1.7
作者：
Jiang, G.;Evans, J.;Chute, C. G.
通讯作者：
Chute, C. G.
影响因子：
10.7
作者：
Cook, T. M.;El-Boghdadly, K.;Higgs, A.
通讯作者：
Higgs, A.
DOI：
10.1164/rccm.2020c1
发表时间：
2020-05-15
影响因子：
24.7
作者：
Jamil, Shazia;Mark, Nick;Pasnick, Susan
通讯作者：
Pasnick, Susan