ArticleJMIR medical informatics2021
Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- COVID-19 severity analysis for clinical decision support based on machine learning approach.Scientific reports · 2025Article
- Tabular transformer generative adversarial network for heterogeneous distribution in healthcare.Scientific reports · 2025Article
- Consensus Between Radiologists, Specialists in Internal Medicine, and AI Software on Chest X-Rays in a Hospital-at-Home Service: Prospective Observational Study.JMIR formative research · 2024Observational
- Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions.Frontiers in robotics and AI · 2024Review
- Artificial Intelligence Methods.Advances in experimental medicine and biology · 2024Review
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study.JMIR mHealth and uHealth · 2023Article
- Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.Frontiers in artificial intelligence · 2023Article
- Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare.Cell reports. Medicine · 2022Review
- Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling.Sensors (Basel, Switzerland) · 2022Article
- MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches.Bioengineering (Basel, Switzerland) · 2022Article
- Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic.International journal of medical informatics · 2022Article
- CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.Applied soft computing · 2022Article
- COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images.Scientific reports · 2022Article
- Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments.Computational intelligence and neuroscience · 2022Article
- The COVID-19 Pandemic and the Need for an Integrated and Equitable Approach: An International Expert Consensus Paper.Thrombosis and haemostasis · 2021Article
- Clinical Utility and Functionality of an Artificial Intelligence-Based App to Predict Mortality in COVID-19: Mixed Methods Analysis.JMIR formative research · 2021Article
- Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study.JMIR medical informatics · 2021Article
- A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19.Journal of clinical medicine · 2021Review
- Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada.Frontiers in artificial intelligence · 2021Article
Corrections and comments
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14 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated.
objectiveThis study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data.
methodsClinical data-including demographics, signs, symptoms, comorbidities, and blood test results-and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework.
resultsImaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929).
conclusionsClinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.
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