Unified AI Framework for Predictive Pediatric Healthcare in Multilingual, Resource-Limited Settings

Abstract

This paper presents the Integrated Predictive Intelligence Tool for Pediatric Health (IPITPH), a mobile-enabled AI-driven decision support system for addressing complex pediatric healthcare challenges in resource-limited, multilingual settings. The system comprises four integrated modules: a hybrid LSTM-DNN predictive analytics module incorporating culturally specific dietary patterns and temporal growth signals for pediatric risk prediction, an LLM-based nutrition optimization module generating personalized meal plans aligned with clinical guidelines and caregiver behavior, a multimodal emergency response module for real-time triage and teleconsultation, and a Retrieval-Augmented Generation-based multilingual conversational AI enabling voice-first caregiver interaction in Sinhala, Tamil, and English. Evaluated on pediatric data from 69 Sri Lankan families supplemented by the PIC clinical database, the predictive module achieved 3.18% MAPE for height prediction and 0.975 AUC for multi-domain risk classification. The nutrition module demonstrated high caloric precision and behavioral adaptation, the emergency module achieved robust triage accuracy with sub-100 ms edge latency, and the conversational module showed significant improvements in clinical accuracy and WHO guideline alignment across all three languages. These results establishing IPITPH as a promising unified AI framework for community-based pediatric healthcare in resource-limited regional contexts, pending broader prospective validation.