ArticleScientific reports2023
Development and validation of a hybrid deep learning-machine learning approach for severity assessment of COVID-19 and other pneumonias.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Severe COVID-19 in the Republic of Korea: Epidemiology, Risk Factors, Therapeutics, and Prognostic Models From Nationwide Data.Journal of Korean medical science · 2026Review
- Development of a computed tomography radiomics and CD38 integrated model: predicting immunotherapy response and investigating biological implications in non-small cell lung cancer.Journal of thoracic disease · 2026Article
- Machine Learning Accurately Predicts Need for Critical Care Support in Patients Admitted to Hospital for Community-Acquired Pneumonia.Critical care explorations · 2025Observational
- Machine Learning Based Multi-Parameter Modeling for Prediction of Post-Inflammatory Lung Changes.Diagnostics (Basel, Switzerland) · 2025Article
- An investigation of the relationship between sPD-1, sPD-L1 and severe pneumonia patients admitted to ICU and its clinical significance.Frontiers in medicine · 2025Article
- Deep Learning-Based Slice Thickness Reduction for Computer-Aided Detection of Lung Nodules in Thick-Slice CT.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study.Diagnostics (Basel, Switzerland) · 2024Article
- A multistage framework for respiratory disease detection and assessing severity in chest X-ray images.Scientific reports · 2024Article
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7 authors.
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Abstract
The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia.
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