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

Key Points

• The XGBoost-based model effectively identifies high-risk children with pneumonia, enhancing clinical decision-making.
• Key predictors for high-risk assessment include PCT, CRP, and respiratory rate components.
• Observational analysis utilizes clinical indicators to construct the risk prediction model, integrating ML-driven interactions.
• The findings highlight the potential for personalized management in pediatric pneumonia cases, leveraging advanced analytics.

Abstract