An explainable deep learning framework for biosensing data interpretation in biomedical engineering and real-time health diagnostics

Evaluations on four biosensing datasets show that our framework achieves superior diagnostic accuracy (up to 92.48%) and AUC (up to 93.65%), outperforming several transformer-based baselines such as RoBERTa and T5. Furthermore, the model provides transparent uncertainty estimates, making it suitable for deployment in clinical and wearable scenarios. By integrating physiological semantics and model interpretability, our framework bridges the gap between black-box AI and trustworthy biomedical intelligence.