ArticleFrontiers in cellular and infection microbiology2022
A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile.
Article in Frontiers in cellular and infection microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis 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
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- S100 Protein and Interleukin Biomarkers Among COVID-19 Subjects With and Without Pneumonia: A Systematic Review and Meta-Analysis.British journal of biomedical science · 2025Pooled it
- Can machine learning support infection control measures by predicting carbapenemase-producing Enterobacterales colonization at admission?Infection control and hospital epidemiology · 2026Article
- Machine Learning Model Predicts Monoclonal Gammopathy Using Routine Laboratory Values.JCO clinical cancer informatics · 2026Article
- Explainable Artificial Intelligence in Healthcare: Current Landscape, Challenges, and Future Directions.Health science reports · 2026Review
- A Reproducible Post-Valve-Replacement EHR Cohort for Comparative AI Studies.Diagnostics (Basel, Switzerland) · 2026Article
- Identification of age-specific urinary metabolic biomarkers in Wilson disease using machine learning: a comparative study of ensemble tree models.Open medicine (Warsaw, Poland) · 2026Article
- Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets.Scientific reports · 2025Article
- A Novel Knowledge Fusion Ensemble for Diagnostic Differentiation of Pediatric Pneumonia and Acute Bronchitis.Diagnostics (Basel, Switzerland) · 2025Article
- Exploring the gut microbiota associated with peripheral nerve invasion in colorectal cancer patients and constructing predictive models.BMC microbiology · 2025Article
- Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.BMC cardiovascular disorders · 2025Article
- Advancing risk factor identification for pediatric lobar pneumonia: the promise of machine learning technologies.Frontiers in pediatrics · 2025Article
- Evaluating machine learning approaches for host prediction using H3 influenza genomic data.PloS one · 2025Article
- Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective.Journal of nursing management · 2025Observational
- Using machine learning methods to investigate the impact of comorbidities and clinical indicators on the mortality rate of COVID-19.Frontiers in medical technology · 2025Article
- A multicenter study on developing a prognostic model for severe fever with thrombocytopenia syndrome using machine learning.Frontiers in microbiology · 2025Article
- Implementation of a Machine Learning Approach Evaluating Risk Factors for Complications after Single-Stage Augmentation Mastopexy.Aesthetic plastic surgery · 2024Article
- Development and validation of machine learning-based prediction model for severe pneumonia: A multicenter cohort study.Heliyon · 2024Article
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Immunoprofiles of COVID-19 uniquely differentiated from other viruses: A machine learning investigation of multiplex immunoassay data.PNAS nexus · 2024Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients. Methods: Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis. Results: Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4 Conclusions: XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.
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.