Open AccessMachine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data
Author(s) -
Subhanik Purkayastha,
Yanhe Xiao,
Zhicheng Jiao,
Rujapa Thepumnoeysuk,
Kasey Halsey,
Jing Wu,
Thi Thanh Van Tran,
B.J. Hsieh,
Ji Whae Choi,
Dongcui Wang,
Martin Vallières,
Robin Wang,
Scott Collins,
Feng Xue,
Michael D. Feldman,
Paul J. Zhang,
Michael K. Atalay,
Ronnie Sebro,
Li Yang,
Yong Fan,
Wei Liao,
Harrison X. Bai
Publication year - 2021
Publication title -
korean journal of radiology/korean journal of radiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.08
H-Index - 57
eISSN - 2005-8330
pISSN - 1229-6929
DOI - 10.3348/kjr.2020.1104
Subject(s) - medicine , concordance , receiver operating characteristic , severity of illness , covid-19 , cohort , radiology , disease , infectious disease (medical specialty)
To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.