Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Dayan I

PROVIDER: S-EPMC9157510 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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

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

Nature medicine 20210915 10

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 a ...[more]

PMID: 34526699

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Project description:ObjectivesFor optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes.MethodsIn this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis.ResultsOf all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified.ConclusionsConsidering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management.Advances in knowledgeWe report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.

Dataset Information

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

Publications

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

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