ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Health Informatics
Machine Learning Prediction of Oxygen Therapy in Pediatric Mycoplasma pneumoniae Pneumonia
Claudio Coppola 1,2
Judith Jeyafreeda Andrew 3,4
Martino Ruggieri 5
Milena La Spina 6
Maria Rosaria La Bianca 2
Salvatore Leonardi 7
Study Group N/A 2
1. Postgraduate Training Programme in Pediatrics, Department of Clinical and Esperimental Medicine, University of Catania, Catania, Italy
2. Unit of Pediatrics, PO "S. Antonio Abate", Trapani, Italy
3. Clinical Bioinformatics Laboratory, INSERM UMR1163, Imagine Institute, Université Paris Cité, Paris F-75006, Paris, France
4. PR[AI]RIE AI Institute, 2 rue Simone Iff, 75012, Paris, France
5. Unit of Pediatric Clinic, Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
6. Unit of Pediatrics and Pediatric Emergency, AOU "Policlinico-San Marco", PO "San Marco", Catania, Italy
7. Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
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Abstract
Background Mycoplasma pneumoniae pneumonia represents a significant cause of community-acquired pneumonia in children, with clinical presentations ranging from mild to severe forms requiring respiratory support. Early identification of children at risk for oxygen therapy remains challenging using conventional clinical and laboratory parameters. Methods We conducted a multicenter retrospective study involving 206 pediatric patients (aged 1 month to 18 years) with confirmed Mycoplasma pneumoniae pneumonia admitted to three Italian hospitals between 2023-2025. Nine machine learning algorithms were developed and validated using routine admission data including demographics, clinical presentation, laboratory tests, and imaging findings. The primary outcome was the need for oxygen therapy during hospitalization. Model performance was evaluated using area under the curve (AUC), precision, recall, and F1-score metrics. Feature importance was assessed using SHAP (Shapley Additive Explanations) analysis. Results Among the 206 patients, 42 (20.4%) required oxygen therapy during hospitalization. The cohort had a mean age of approximately 4.6 years (SD ≈ 3.5), with comorbidities present in approximately 40% of cases. Support Vector Machine (SVM) achieved the highest performance with an AUC of 0.97, precision of 0.93, recall of 0.93, and F1-score of 0.92. Logistic Regression (AUC 0.95), XGBoost (AUC 0.94), and LightGBM (AUC 0.93) also demonstrated strong predictive performance. SHAP analysis consistently identified C-reactive protein (CRP), lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), neutrophil percentage, and respiratory distress as the most important predictive features across models. Conclusion Machine learning models using routine admission data can accurately predict oxygen therapy requirements in pediatric Mycoplasma pneumoniae pneumonia. The integration of interpretable artificial intelligence approaches may enable earlier risk stratification and improve clinical decision-making in pediatric respiratory infections.
Summary
Keywords
artificial intelligence, machine learning, Mycoplasma pneumoniae, Oxygen therapy, Pediatric pneumonia, Predictive Modeling, Respiratory support, Shap
Received
27 November 2025
Accepted
09 January 2026
Copyright
© 2026 Coppola, Andrew, Ruggieri, La Spina, La Bianca, Leonardi and N/A. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Claudio Coppola; Judith Jeyafreeda Andrew
Disclaimer
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