ArticleComputers in biology and medicine2021
Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 63 citations in OpenAlex.
- Role of Galanin system and insulin resistance parameters as predictive tools for diagnosis of Long-COVID patients.Biochemistry and biophysics reports · 2025Article
- Diagnosis, Severity, and Prognosis from Potential Biomarkers of COVID-19 in Urine: A Review of Clinical and Omics Results.Metabolites · 2024Review
- Back to Basics: The Diagnostic Value of a Complete Blood Count in the Clinical Management of COVID-19.Diagnostics (Basel, Switzerland) · 2024Article
- Urine Parameters in Patients with COVID-19 Infection.Life (Basel, Switzerland) · 2023Review
- Association between SUMF1 polymorphisms and COVID-19 severity.BMC genomic data · 2023Article
- Article
- Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks.Journal of education and health promotion · 2023Article
- Machine Learning Successfully Detects Patients with COVID-19 Prior to PCR Results and Predicts Their Survival Based on Standard Laboratory Parameters in an Observational Study.Infectious diseases and therapy · 2023Article
- Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms.Journal of biomedical physics & engineering · 2022Article
- Predictive models for COVID-19 detection using routine blood tests and machine learning.Heliyon · 2022Review
- Development of neural network models for prediction of the outcome of COVID-19 hospitalized patients based on initial laboratory findings, demographics, and comorbidities.Journal of family medicine and primary care · 2022Article
- Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.Computers in biology and medicine · 2022Article
- Predicting COVID-19 outcomes from clinical and laboratory parameters in an intensive care facility during the second wave of the pandemic in South Africa.IJID regions · 2022Article
- CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.Applied soft computing · 2022Article
- Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study.Medical journal of the Islamic Republic of Iran · 2022Article
- Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.Computational intelligence and neuroscience · 2022Article
- Predicting Severe Disease and Critical Illness on Initial Diagnosis of COVID-19: Simple Triage Tools.Frontiers in medicine · 2022Article
- Predicting the Disease Severity of Virus Infection.Advances in experimental medicine and biology · 2022Article
- A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest.Informatics in medicine unlocked · 2022Article
- Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction.Journal of personalized medicine · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity.
methodsCOVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients' laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve).
resultsThe first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase.
conclusionOur analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.
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