ArticleInfection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases2021
Machine learning predictive model for severe COVID-19.
Article in Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Topographical Distribution of Neuroanatomical Abnormalities Following COVID-19 Invasion : A Systematic Literature Review.Clinical neuroradiology · 2024Pooled it
- The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis.International journal of medical informatics · 2022Pooled it
- Subtle changes on electrocardiogram in severe patients with COVID-19 may be predictors of treatment outcome.Frontiers in artificial intelligence · 2025Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach.BMC infectious diseases · 2023Article
- Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.Sensors (Basel, Switzerland) · 2023Review
- Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in south Florida.Frontiers in digital health · 2023Article
- A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical characteristics and outcomes of hospitalized patients with COVID-19 in a Brazilian hospital: a retrospective study of the first and second waves.IJID regions · 2022Article
- Clinical Characteristics Associated with Bacterial Bloodstream Coinfection in COVID-19.Infectious diseases and therapy · 2022Article
- Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System.Journal of personalized medicine · 2022Article
- An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples.Sensors (Basel, Switzerland) · 2022Article
- Using random forest algorithm for glomerular and tubular injury diagnosis.Frontiers in medicine · 2022Article
- Statistical Analysis and Machine Learning Prediction of Disease Outcomes for COVID-19 and Pneumonia Patients.Frontiers in cellular and infection microbiology · 2022Article
- A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19.Journal of clinical medicine · 2021Review
- Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.Frontiers in digital health · 2021Article
- Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction.Frontiers in public health · 2021Article
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji Medical College, China. A total of 151 cases from Jan. 26 to Mar. 20, 2020, were included. Then we followed 5 steps to predict and evaluate the model: data preprocessing, data splitting, feature selection, model building, prevention of overfitting, and Evaluation, and combined with artificial neural network algorithms. We processed the results in the 5 steps. In feature selection, ALB showed a strong negative correlation (r = 0.771, P < 0.001) whereas GLB (r = 0.661, P < 0.001) and BUN (r = 0.714, P < 0.001) showed a strong positive correlation with severity of COVID-19. TensorFlow was subsequently applied to develop a neural network model. The model achieved good prediction performance, with an area under the curve value of 0.953(0.889-0.982). Our results showed its outstanding performance in prediction. GLB and BUN may be two risk factors for severe COVID-19. Our findings could be of great benefit in the future treatment of patients with COVID-19 and will help to improve the quality of care in the long term. This model has great significance to rationalize early clinical interventions and improve the cure rate.
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