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November 28, 2025Scientific ReportsOpen Access

Development of a machine learning model to diagnose pediatric lower respiratory tract infections

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Authors

ALAh Ra Lee HLHyunju Lee YCYoungmin Cho

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Implication

Retrospective cohort study shows machine learning improves diagnostic performance in pediatric lower respiratory infections, indicating reduced antibiotic use.

Key Points

The random forest model achieved an AUROC of 0.953 for diagnosing lower respiratory infections.
Overall, 8,583 pediatric patients were included after data preprocessing and undersampling.
The study used a two-step machine learning approach to classify bacterial and viral pathogens.
Implementing this model could significantly lower antibiotic prescriptions and combat antibiotic resistance.

Cite This Study

Lee et al. (2025) studied this question.

synapsesocial.com/papers/6928f106a65b730b9ea79d4d https://doi.org/10.1038/s41598-025-25677-y

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Implication

Retrospective cohort study shows machine learning improves diagnostic performance in pediatric lower respiratory infections, indicating reduced antibiotic use.

Development of a machine learning model to diagnose pediatric lower respiratory tract infections

Discussion

Member takes

Implication

Key Points

Cite This Study

Development of a machine learning model to diagnose pediatric lower respiratory tract infections

Discussion

Member takes

Implication

Key Points

Cite This Study

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