ArticleFrontiers in cell and developmental biology2020
Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it, 129 citations in OpenAlex.
- Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- Artificial Intelligence for COVID-19: A Systematic Review.Frontiers in medicine · 2021Pooled it
- COVID-19 severity analysis for clinical decision support based on machine learning approach.Scientific reports · 2025Article
- Rapid discrimination ofBMJ health & care informatics · 2025Article
- Biotechnological Preparedness for Novel Pandemics: Diagnostic Performance of IVDS Against SARS-CoV-2.MicrobiologyOpen · 2025Review
- Prediction models based on machine learning algorithms for COVID-19 severity risk.BMC public health · 2025Article
- Topological data analysis and machine learning for COVID-19 detection in CT scan lung images.BMC biomedical engineering · 2025Article
- Developing a machine learning model with enhanced performance for predicting COVID-19 from patients presenting to the emergency room with acute respiratory symptoms.IET systems biology · 2024Article
- Article
- Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables.Scientific reports · 2024Article
- Construction and validation of risk prediction models for pulmonary embolism in hospitalized patients based on different machine learning methods.Frontiers in cardiovascular medicine · 2024Article
- A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients.PeerJ. Computer science · 2024Article
- An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease.International journal of telemedicine and applications · 2024Article
- Nanostructures for prevention, diagnosis, and treatment of viral respiratory infections: from influenza virus to SARS-CoV-2 variants.Journal of nanobiotechnology · 2023Review
- Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs.Scientific reports · 2023Article
- Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients.Journal of integrative bioinformatics · 2023Observational
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.Diagnostics (Basel, Switzerland) · 2023Review
- SARS-CoV-2 Prediction Strategy Based on Classification Algorithms from a Full Blood Examination.TheScientificWorldJournal · 2023Article
- Integrated Bayesian and association-rules methods for autonomously orienting COVID-19 patients.Medical & biological engineering & computing · 2022Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.