ArticleJournal of medical virology2022
Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.
Article in Journal of medical virology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Artificial Intelligence Integrated Smart Medical Imaging Lab Framework for Enhanced Diagnosis and Treatment of Pandemic-Prone Diseases.Health science reports · 2026Article
- An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis.Scientific reports · 2024Article
- Rapid Triage of Children with Suspected COVID-19 Using Laboratory-Based Machine-Learning Algorithms.Viruses · 2023Article
- Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.BMC bioinformatics · 2023Article
- Predictive models for COVID-19 detection using routine blood tests and machine learning.Heliyon · 2022Review
- Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.Journal of medical virology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from January 2020 to June 2021) to construct different machine learning (ML) models to classify patients into either mild or severe cases of COVID-19. All models show good performance in the classification between COVID-19 patients into mild and severe disease. The area under the curve (AUC) of the random forest model is 0.89, the AUC of the naive Bayes model is 0.90, the AUC of the support vector machine model is 0.86, and the AUC of the KNN model is 0.78, the AUC of the Logistic regression model is 0.84, and the AUC of the artificial neural network model is 0.87, among which the naive Bayes model has the best performance. Different ML models can classify patients into mild and severe cases based on 38 blood test indicators taken on the first day of admission for patients diagnosed with COVID-19.
Indexed as
Identifiers
What OpenQuestion holds
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.