A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization
Document Type
Article
Publication Title
JAMA Network Open
Abstract
Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups.
First Page
e2124946
DOI
10.1001/jamanetworkopen.2021.24946
Publication Date
10-11-2021
Recommended Citation
Yan, Yao; Schaffter, Thomas; Bergquist, Timothy; Yu, Thomas; Prosser, Justin; Aydin, Zafer; Jabeer, Amhar; Brugere, Ivan; Gao, Jifan; Chen, Guanhua; Causey, Jason L.; Yao, Yuxin; Bryson, Kevin; Long, Dustin R.; Jarvik, Jeffrey G.; Lee, Christoph I.; Wilcox, Adam; Guinney, Justin; Mooney, Sean; Jujjavarapu, Chethan; Thomas, Jason; Gunn, Martin; Wu, YiFan; Dobbins, Nicholas; O′Reilly-Shah, Vikas; Teng, Andrew; Hammarlund, Noah; Nichol, Graham; Brandt, Pascal; Pejaver, Vikas; Britt, Beth; and Guan, Yuanfang, "A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization" (2021). Center for No Boundary Thinking. 5.
https://arch.astate.edu/cnbt/5
