ArticleMedical image analysis2021
Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Risk factors for severe COVID-19 and development of a predictive model.BMC pulmonary medicine · 2025Article
- Deep learning for classifying imaging patterns of interstitial lung disease associated with idiopathic inflammatory myopathies.Scientific reports · 2025Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- uRP: An integrated research platform for one-stop analysis of medical images.Frontiers in radiology · 2023Article
- McS-Net: Multi-class Siamese network for severity of COVID-19 infection classification from lung CT scan slices.Applied soft computing · 2022Article
- Predicting medical events and ICU requirements using a multimodal multiobjective transformer network.Experimental biology and medicine (Maywood, N.J.) · 2022Article
- Non-iterative learning machine for identifying CoViD19 using chest X-ray images.Scientific reports · 2022Article
- Article
- Survival Risk Prediction of Esophageal Squamous Cell Carcinoma Based on BES-LSSVM.Computational intelligence and neuroscience · 2022Article
- Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation.IEEE transactions on medical imaging · 2022Article
- Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.Applied soft computing · 2022Article
- COVIDXception-Net: A Bayesian Optimization-Based Deep Learning Approach to Diagnose COVID-19 from X-Ray Images.SN computer science · 2022Article
- Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.Information processing & management · 2022Article
- DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021Article
- The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19.Sustainable cities and society · 2021Article
- Federated learning for predicting clinical outcomes in patients with COVID-19.Nature medicine · 2021Article
- The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.Diagnostics (Basel, Switzerland) · 2021Review
- Brain functional connectivity analysis based on multi-graph fusion.Medical image analysis · 2021Article
- Circulating Calprotectin as a Biomarker of COVID-19 Severity.Expert review of clinical immunology · 2021Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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