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PneumoMLPred: Innovating pneumonia management with guideline adherence review and explainable machine learning-based
Kamel A Samara1, Hiba Jawdat Barqawi2, Saryia Adra3
1Jefferson Abington Hospital, Abington, PA, United States of America.
Respiratory Medicine
|May 20, 2026
Summary
Community-acquired pneumonia (CAP) management shows low guideline adherence. A new ML model, PneumoMLPred, accurately predicts in-hospital mortality using factors like PSI and CRP.
Area of Science:
- Medical research
- Infectious diseases
- Machine learning in healthcare
Background:
- Community-acquired pneumonia (CAP) is a leading global cause of death.
- Rising antibiotic resistance and aging populations exacerbate CAP burden.
- Existing CAP mortality prediction models are limited.
Purpose of the Study:
- Evaluate adherence to 2019 American Thoracic Society/Infectious Diseases Society of America (ATS/IDSA) CAP management guidelines.
- Develop and validate a machine learning (ML)-based model for predicting in-hospital CAP mortality.
Main Methods:
- Retrospective chart review of 783 CAP cases (2016-2021).
- Analysis of demographics, comorbidities, vitals, labs, imaging, and antibiotic regimens.
- Machine learning models (Random Forest, SVM) for prediction; SHAP for interpretability.
Main Results:
- Overall mortality rate was 15.75% for high-severity (PSI Class IV/V) CAP cases.
- Less than one-third of cases adhered to 2019 ATS/IDSA guidelines; antibiotic dosing was often suboptimal.
- PneumoMLPred (SVM-based) achieved 88.5% accuracy, 96.3% specificity, and 0.87 AUROC.
- SHAP analysis identified Pneumonia Severity Index (PSI), C-reactive protein (CRP), and kidney disease as key mortality predictors.
Conclusions:
- Adherence to 2019 ATS/IDSA CAP management guidelines is significantly limited.
- PneumoMLPred demonstrates strong potential for accurate in-hospital CAP mortality prognostication.
- Explainable AI (SHAP) provides insights into key drivers of CAP mortality.
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