ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
This article is part of the Research TopicExplainable Models in Biosensors, Biosensing Technology, and Biomedical EngineeringView all 5 articles
An Explainable Deep Learning Framework for Biosensing Data Interpretation in Biomedical Engineering and Real-Time Health Diagnostics
Provisionally accepted
- Department of Gastroenterology, Affiliated Hospital of North China University of Technology, Tangshan, China
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This work proposes an explainable deep learning framework to transform complex biosignal dynamics into interpretable health assessments. The core of our approach is the PhysioGraph Inference Network (PGIN), which combines temporal graph reasoning with probabilistic modeling to capture dynamic inter-sensor dependencies under physiological priors. To further enhance adaptability, an Adaptive Health State Inference Mechanism (AHSIM) is introduced to adjust diagnostic granularity based on uncertainty and signal entropy. 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.
Keywords: Explainable deep learning, PhysioGraph Inference Network (PGIN), Adaptive Health State Inference Mechanism (AHSIM), Diagnostic accuracy, Uncertainty Estimation
Received: 19 Aug 2025; Accepted: 15 Dec 2025.
Copyright: © 2025 Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Heng Zhang
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