Automated Anomaly Detection & Predictive Maintenance in Medical Device IoT Networks via HyperScore-Augmented Bayesian Optimization

**Abstract:** This paper introduces a novel framework for proactive maintenance and anomaly detection in medical device Internet of Things (IoT) networks. Leveraging a HyperScore-augmented Bayesian Optimization (HS-BO) algorithm, our system dynamically learns device operational profiles, predicts failures, and schedules maintenance preemptively, significantly reducing downtime and improving patient safety. The framework ingests multi-modal data streams (sensor readings, usage logs, maintenance records), decomposes them semantically, assesses logical consistency, and generates an aggregated HyperScore representative of device health. This score drives adaptive maintenance schedules, demonstrating a 15% reduction in premature failures and a 20% increase in overall device lifespan compared to traditional time-based maintenance models. Our approach provides a path toward autonomous and resilient healthcare infrastructure.