TY - JOUR
T1 - Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan
AU - Zhu, Xiaofeng
AU - Song, Bin
AU - Shi, Feng
AU - Chen, Yanbo
AU - Hu, Rongyao
AU - Gan, Jiangzhang
AU - Zhang, Wenhai
AU - Li, Man
AU - Wang, Liye
AU - Gao, Yaozong
AU - Shan, Fei
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grants 2018AAA0102200 and 2018YFC0116400, the National Natural Science Foundation of China under Grant 61876046 , the Sichuan Science and Technology Program under Grants 2018GZDZX0032 and 2019YFG0535 , and the Novel Coronavirus Special Research Foundation of the Shanghai Municipal Science and Technology Commission under Grant 20441900600.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/1
Y1 - 2021/1
N2 - With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians’ workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers’ influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients’ lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.
AB - With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the conversion time that patients possibly convert to the severe stage, for designing effective treatment plans and reducing the clinicians’ workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time formulated as a classification task, and if yes, the conversion time will be predicted formulated as a classification task. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers’ influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of the high-dimensional data and learn the shared information across two tasks, i.e., the classification and the regression. To our knowledge, this study is the first work to jointly predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients’ lives. Experimental analysis was conducted on a real data set from two hospitals with 408 chest computed tomography (CT) scans. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the conversion time.
KW - CT Scan data
KW - Coronavirus disease
KW - Feature selection
KW - Imbalance classification
KW - Sample selection
UR - http://www.scopus.com/inward/record.url?scp=85092938384&partnerID=8YFLogxK
U2 - 10.1016/j.media.2020.101824
DO - 10.1016/j.media.2020.101824
M3 - Article
C2 - 33091741
AN - SCOPUS:85092938384
VL - 67
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
M1 - 101824
ER -