Background: Pneumonia is a leading cause of mortality in children aged <5 years. While machine learning (ML) has been applied to pneumonia diagnostics, few studies have focused on predicting the need for escalation of care in pediatric cases. This study aims to develop an ML-based clinical decision support tool for predicting the need for escalation of care in community-acquired pneumonia cases. Objective: The primary objective was to develop a robust predictive tool to help primary care physicians determine where and how a case should be managed. Methods: Data from 437 children with community-acquired pneumonia, collected before the COVID-19 pandemic, were retrospectively analyzed. Pediatricians encoded key clinical features from unstructured medical records based on Integrated Management of Childhood Illness guidelines. After preprocessing with Synthetic Minority Oversampling Technique–Tomek to handle imbalanced data, feature selection was performed using Shapley additive explanations values. The model was optimized through hyperparameter tuning and ensembling. The primary outcome was the level of care severity, defined as the need for referral to a tertiary care unit for intensive care or respiratory support. Results: A total of 437 cases were analyzed, and the optimized models predicted the need for transfer to a higher level of care with an accuracy of 77% to 88%, achieving an area under the receiver operator characteristic curve of 0.88 and an area under the precision-recall curve of 0.96. Shapley additive explanations value analysis identified hypoxia, respiratory distress, age, weight-for-age z score, and complaint duration as the most important clinical predictors independent of laboratory diagnostics. Conclusions: This study demonstrates the feasibility of applying ML techniques to create a prognostic care decision tool for childhood pneumonia. It provides early identification of cases requiring escalation of care by combining foundational clinical skills with data science methods.

Background Pneumonia is the leading cause of preventable mortality under five years of age. Appropriate case management is as essential as disease prevention interventions, especially in primary care settings. Computer science has been used accurately and widely for pneumonia diagnosis; however, prognosis studies are relatively low. Herein, we developed a machine learning-based clinical decision support system tool for childhood pneumonia to provide prognostic support for case management. Methods We analyzed data from 437 children admitted to our clinic with a pneumonia diagnosis between 2014 and 2020. Pediatricians encoded the raw dataset according to candidate features. Before the experimental study of the machine learning algorithms of Pycaret, SMOTE-Tomek was utilized for managing imbalanced datasets. The feature selection was made by examining the SHAP values of the algorithm with the highest performance and re-modeled with the most important clinical features. We optimized hyperparameters and employed ensemble methods to develop a robust predictive model. Results Optimized models predicted pneumonia prognosis with %77-88 accuracy. It was shown that severity could be determined over %84 by five clinical features: hypoxia, respiratory distress, age, Z score of weight for age, and antibiotic usage before admission. Conclusions In this experimental study, we demonstrated that contemporary data science methods, such as oversampling, feature selection, and machine learning tools, are promising in predicting the critical care need of patients. Even in small-size samples like our study, ML methods can reach current wisdom. Highlights Pneumonia accounts for 14% of mortality in children under 5, with over 740,000 deaths in 2019 alone. WHO and UNICEF’s GAPPD aims to cut mortality rates by focusing on vaccinations, sanitation, breastfeeding, and addressing pediatric HIV. Accurate diagnosis and timely treatment can reduce pneumonia mortality by up to 28%, yet diagnosing can be challenging. Many in underdeveloped regions lack access to essential equipment and trained staff, exacerbating mortality rates. Data science and machine learning offer promising solutions for pneumonia management, especially in LMICs, with a focus on prognostic support.

The catastrophic earthquakes that struck Southern Turkey in 2023 highlighted the pressing need for effective disaster management strategies. The unprecedented scale of the crisis tested the robustness of traditional healthcare responses and highlighted the potential of e-health solutions. Despite the deployment of Emergency Medical Teams, initial responders - primarily survivors of the earthquakes - faced an enormous challenge due to their lack of training in mass-casualty situations. An e-health platform was introduced to support these first responders, offering tools for drug calculations, case management guidelines, and a deep learning model for pediatric X-ray analysis. This commentary presents an analysis of the platform’s use and contributes to the growing discourse on integrating digital health technologies in disaster response and management.

Objective: Pediatric unintentional injuries are a major public health concern, as most are preventable. This study aimed to assess emergency department cases by etiology, demographics, seasonal distribution, and outcomes, to define the regional profile and guide prevention. Material and Methods: This retrospective study included 2524 children aged 0–18 years admitted with unintentional injuries between January 2021 and June 2024. Cases were classified as trauma, poisoning/aspiration, environmental injuries, or other causes. Results: Of the cases, 1454 (57.6%) were male, median age 47 months. Poisoning/aspiration was most common (n=1153; 45.6%), followed by trauma (n=840; 33.2%), environmental injuries (n=473; 18.7%), and others (n=62; 2.4%). Poisonings predominated in 0–5 years, while trauma was more frequent in adolescents. Leading poisoning causes were drugs (n=516; 20.4%) and corrosives (n=441; 17.5%). Carbon monoxide poisoning was notable among environmental injuries (n=267; 10.6%). Most patients were discharged (80.6%), while 14.5% were hospitalized, 3.7% required intensive care, and 5.5% underwent surgery. Mortality and sequelae were each 0.3%. Seasonal variation was evident: poisonings in spring–summer, trauma in summer–autumn, and carbon monoxide in winter. Conclusion: Poisonings were the leading cause in early childhood, while trauma predominated in adolescents. Preventive strategies, including parental education, household safety, and region-specific policies, are essential to reduce morbidity and mortality.