Transformer-based Mobile Health Text Analytics System: Intelligent Symptom Monitoring and Alert for Pervasive Healthcare Environments

Abstract

Healthcare accessibility challenges disproportionately affect underserved populations, with communication barriers between patients and providers contributing to diagnostic errors and suboptimal outcomes. This study develops and validates a transformer-based lightweight mobile health text analytics system for intelligent symptom monitoring in pervasive healthcare environments. The system employs a DistilBERT-based architecture compressed to 45MB, integrated with medical knowledge graphs incorporating ICD-10 and SNOMED CT standards, and trained on 15,000 medical records from ten hospitals. A three-tier pervasive computing architecture enables cross-platform deployment across iOS, Android, and HarmonyOS, while a four-tier risk stratification framework classifies conditions into self-observation (70%), community consultation (20%), hospital evaluation (8%), and emergency intervention (2%) categories. Privacy preservation utilizes federated learning with differential privacy mechanisms. Clinical effectiveness was evaluated through a randomized controlled trial involving 1,500 participants across diverse demographics. Results demonstrated 86.8% diagnostic concordance versus 70.2% in controls, achieving 93.7% sensitivity and 98.4% specificity for critical symptoms, while reducing emergency department visits by 35.7% and achieving $847 cost savings per patient. Patient experience improvements included 82.7 System Usability Scale scores and 78.4% sustained engagement. This research establishes a paradigm for responsible AI deployment in healthcare that prioritizes clinical effectiveness and social responsibility, contributing to universal health coverage through innovative, accessible, and ethically sound technologies.

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