AIBullisharXiv – CS AI · 7h ago7/10
🧠
DEM: A Distilled Explanation Model for Interpretable Anomaly Detection in Physiological Sensor Networks
Researchers propose DEM (Distilled Explanation Model), a glass-box framework for anomaly detection in physiological sensor networks that distills gradient boosting expertise into interpretable decision trees while maintaining high accuracy (AUC 0.9964). The model achieves 1235x faster inference than SHAP-based methods, making it practical for real-time medical monitoring with clinically meaningful explanations rather than post-hoc approximations.