ArticleHeliyon2024
Development and validation of machine learning-based prediction model for severe pneumonia: A multicenter cohort study.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Predictive value of the prognostic nutritional index and aggregate index of systemic inflammation for pulmonary complications in children with influenza.Translational pediatrics · 2026Article
- Machine learning-driven risk assessment of severeFrontiers in medicine · 2026Article
- Development and validation of a nomogram integrating inflammatory-nutritional-metabolic composite indices for predicting short-term adverse outcomes in hospitalized patients with pneumonia.Frontiers in medicine · 2026Article
- Exploration of the correlation between clinical indicators and prognosis in hospitalized children with pneumonia and construction of a risk prediction model based on machine learning algorithms.Frontiers in medicine · 2026Article
- Machine learning-based identification of inflammatory biomarkers for predicting pulmonary consolidation in children with Chlamydia pneumoniae infection.Frontiers in pediatrics · 2026Article
- Staged identification of CAP in fever patients across epidemic environments: modeling & validation.Scientific reports · 2025Article
- Exploring the correlation between eosinophils with clinical risk of severe pneumonia based on MIMIC-IV data.Scientific reports · 2025Article
- Inflammatory and Oxidative Biological Profiles in Mental Disorders: Perspectives on Diagnostics and Personalized Therapy.International journal of molecular sciences · 2025Review
- Development and multi-database validation of interpretable machine learning models for predicting In-Hospital mortality in pneumonia patients: A comprehensive analysis across four healthcare systems.Respiratory research · 2025Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Severe pneumonia (SP) is a prevalent respiratory ailment characterized by high mortality and poor prognosis. Current scoring systems for pneumonia are not only time-consuming but also exhibit limitations in early SP prediction. To address this gap, this study aimed to develop a machine-learning model using inflammatory markers from peripheral blood for early prediction of SP. A total of 204 pneumonia patients from seven medical centers were studied, with 143 (68 SP cases) in the training cohort and 61 (32 SP cases) in the test cohort. Clinical characteristics and laboratory test results were collected at diagnosis. Various models including Logistic Regression, Random Forest, Naïve Bayes, XGBoost, Support Vector Machine, and Decision Tree were built and evaluated. Seven predictors-age, sex, WBC count, T-lymphocyte count, NLR, CRP, TNF-α, IL-4/IFN-γ ratio, IL-6/IL-10 ratio-were selected through LASSO regression and clinical insight. The XGBoost model, exhibiting best performance, achieved an AUC of 0.901 (95 %
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