Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan

Xiaofeng Zhu, Bin Song, Feng Shi, Yanbo Chen, Rongyao Hu, Jiangzhang Gan, Wenhai Zhang, Man Li, Liye Wang, Yaozong Gao, Fei Shan, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)


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.

Original languageEnglish
Article number101824
JournalMedical Image Analysis
Publication statusPublished - 2021 Jan

Bibliographical note

Publisher Copyright:
© 2020 Elsevier B.V.


  • CT Scan data
  • Coronavirus disease
  • Feature selection
  • Imbalance classification
  • Sample selection

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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