ArticleScientific reports2025
Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Revolutionizing sepsis diagnosis using machine learning and deep learning models: a systematic literature review.BMC infectious diseases · 2025Pooled it
- Early Machine Learning-Based Identification of Hospitalized Patients at Low Risk of Respiratory Deterioration or Mortality in Community-Acquired Pneumonia: External Validation of a Multivariable Model.Infectious disease reports · 2026Article
- Breast cancer survival prediction using machine learning and multimodal data for personalized care plan.BMC cancer · 2026Article
- Integrating the Hospital Frailty Risk Score into Explainable Machine Learning to Predict Mortality in Older Adults with Pneumonia: A Chilean Population-Based Study.Diagnostics (Basel, Switzerland) · 2026Article
- 72-hour SOFA changes and risk stratification for invasive mechanical ventilation in patients with community-acquired Pneumonia.Scientific reports · 2026Article
- Machine learning-assisted prognostic model for mortality in ICU patients with culture-confirmedFrontiers in cellular and infection microbiology · 2026Article
- Interpretable machine learning identifies immune-inflammatory and immunothrombotic biomarkers for myocardial injury and mortality risk stratification in severe pneumonia with diverse infectious etiologies.Frontiers in cellular and infection microbiology · 2026Article
- Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction.Current pulmonology reports · 2026Review
- Article
- 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
- Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Towards a holistic approach to pulmonary infections. Insights from the Sixth Annual Meeting of Spanish Experts 2024.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2025Review
- Artificial intelligence can extract important features for diagnosing axillary lymph node metastasis in early breast cancer using contrast-enhanced ultrasonography.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. The model was interpreted using Shapley Additive Explanations. We selected the optimal model to generate the web calculator. A total of 23 predictive features were selected. The Light Gradient Boosting Machine (LightGBM) model had an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CI: 0.757-0.927), with an external 5-fold cross-validation average AUC of 0.842 ± 0.038, which was superior to the other models. External validation results also demonstrated good performance by the LightGBM model with an AUC of 0.856 (95% CI: 0.792-0.921). Based on this, we generated a web calculator by combining five high importance predictive factors. The LightGBM model was confirmed to be efficient and stable in predicting the mortality risk of patients with SCAP admitted to the ICU. The web calculator based on the LightGBM model can provide clinicians with a prognostic evaluation tool.
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