Abstract
BACKGROUND: Early life health risks can shape long-term morbidity trajectories, yet prevailing pediatric risk assessment paradigms are often fragmented and insufficiently capable of integrating heterogeneous data streams into actionable, individualized profiles. OBJECTIVE: This study aimed to design, implement, and validate an artificial intelligence-driven framework that fuses multimodal pediatric data and leverages advanced natural language processing and ensemble learning to improve early, accurate stratification of key pediatric health risks. METHODS: A retrospective dataset of over 40,000 pediatric participants aged 2-8 years was used to train and evaluate the framework. Data were split into training, validation, and test sets (70%, 15%, and 15%, respectively) with a temporally mindful partitioning strategy to approximate prospective evaluation. Baseline comparators included traditional statistical and machine learning models, and the statistical significance of area under the receiver operating characteristic curve (AUC-ROC) differences was assessed using the DeLong test. RESULTS: The proposed Bidirectional Encoder Representations From Transformers-based model achieved an AUC-ROC of 0.85 (95% CI 0.82-0.88), sensitivity of 0.78, specificity of 0.80, and F1-score of 0.75 on the test set, outperforming multiple baseline models. In an additional manual comparison evaluation, automated and expert assessments aligned with 78% accuracy (78/100), and most discrepancies arose in "equivalent" cases. CONCLUSIONS: This study provides a validated, artificial intelligence-driven, multimodal pediatric health risk stratification framework that translates heterogeneous child health data into clinically actionable risk profiles, demonstrating strong discriminative performance and meaningful agreement with expert assessment. The framework supports proactive, individualized pediatric care and offers a scalable foundation for further validation across broader populations and longitudinal follow-up.