A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization

Yan, Yao; Schaffter, Thomas; Bergquist, Timothy; Yu, Thomas; Prosser, Justin; AYDIN, ZAFER; Jabeer, Amhar; Brugere, Ivan; Gao, Jifan; Chen, Guanhua; Causey, Jason; Yao, Yuxin; Bryson, Kevin; Long, Dustin; Jarvik, Jeffrey; Lee, Christoph; Wilcox, Adam; Guinney, Justin; Mooney, Sean

doi:10.1001/jamanetworkopen.2021.24946

A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization

Yan Y., Schaffter T., Bergquist T., Yu T., Prosser J., AYDIN Z., ...Daha Fazla

JAMA NETWORK OPEN, cilt.4, sa.10, 2021 (SCI-Expanded)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 4 Sayı: 10
Basım Tarihi: 2021
Doi Numarası: 10.1001/jamanetworkopen.2021.24946
Dergi Adı: JAMA NETWORK OPEN
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE, Directory of Open Access Journals
Abdullah Gül Üniversitesi Adresli: Evet

Özet

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.