ArticleComputer methods and programs in biomedicine2021
Artificial Intelligence-Based Prediction of Covid-19 Severity on the Results of Protein Profiling.
Article in Computer methods and programs in biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Galectins can serve as biomarkers in COVID-19: A comprehensive systematic review and meta-analysis.Frontiers in immunology · 2023Pooled 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
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- Prediction of COVID-19 Hospitalization and Mortality Using Artificial Intelligence.Healthcare (Basel, Switzerland) · 2024Article
- Neutrophil-to-Lymphocyte Ratio and Cytokine Profiling as Predictors of Disease Severity and Survival in Unvaccinated COVID-19 Patients.Vaccines · 2024Article
- Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.BMC medical informatics and decision making · 2023Article
- Circulating proteins to predict COVID-19 severity.Scientific reports · 2023Article
- Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.Artificial intelligence in medicine · 2023Review
- Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning.Molecular medicine (Cambridge, Mass.) · 2023Article
- Modeling Consequences of COVID-19 and Assessing Its Epidemiological Parameters: A System Dynamics Approach.Healthcare (Basel, Switzerland) · 2023Article
- Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19.Frontiers in medicine · 2023Article
- Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa.Diagnostics (Basel, Switzerland) · 2022Article
- Severe Neuro-COVID is associated with peripheral immune signatures, autoimmunity and neurodegeneration: a prospective cross-sectional study.Nature communications · 2022Article
- Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.International journal of molecular sciences · 2022Article
- Machine learning approaches in Covid-19 severity risk prediction in Morocco.Journal of big data · 2022Article
- Melatonin inhibits lipid accumulation to repress prostate cancer progression by mediating the epigenetic modification of CES1.Clinical and translational medicine · 2021Article
- Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19.Frontiers in physiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 progresses slowly and negatively affects many people. However, mild to moderate symptoms develop in most infected people, who recover without hospitalization. Therefore, the development of early diagnosis and treatment strategies is essential. One of these methods is proteomic technology based on the blood protein profiling technique. This study aims to classify three COVID-19 positive patient groups (mild, severe, and critical) and a control group based on the blood protein profiling using deep learning (DL), random forest (RF), and gradient boosted trees (GBTs).
methodsThe dataset consists of 93 samples (60 COVID-19 patients, 33 control), and 370 variables obtained from an open-source website. The current dataset contains age, gender, and 368 protein, used to predict the relationship between disease severity and proteins using DL and machine learning approaches (RF, GBTs). An evolutionary algorithm tunes hyperparameters of the models and the predictions are assessed through accuracy, sensitivity, specificity, precision, F1 score, classification error, and kappa performance metrics.
resultsThe accuracy of RF (96.21%) was higher as compared to DL (94.73%). However, the ensemble classifier GBTs produced the highest accuracy (96.98%). TGB1BP2 in the cardiovascular II panel and MILR1 in the inflammation panel were the two most important proteins associated with disease severity.
conclusionsThe proposed model (GBTs) achieved the best prediction of disease severity based on the proteins compared to the other algorithms. The results point out that changes in blood proteins associated with the severity of COVID-19 may be used in monitoring and early diagnosis/treatment of the disease.
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.