Deployment of Deep Learning Models for Continuous Patient Monitoring and Predictive Maintenance Strategies to Support Asset Management in Healthcare Infrastructure

Abstract

This research paper presents a novel framework for implementing deep learning techniques to enhance real-time patient monitoring and predictive maintenance of medical equipment in modern healthcare facilities. We propose an integrated system architecture that leverages multilayer neural networks, reinforcement learning algorithms, and edge computing to process continuous streams of biometric and equipment telemetry data. Our approach demonstrates significant improvements in early detection of patient deterioration with a 27.4\% reduction in false alarm rates compared to conventional threshold-based systems. The predictive maintenance component utilizes transformer-based networks to forecast equipment failures with 94.3\% accuracy approximately 48 hours before critical malfunctions occur. We formulate a mathematical model incorporating stochastic differential equations to represent physiological variability and equipment degradation patterns, and demonstrate how deep learning architectures can effectively capture these complex dynamics. Implementation challenges including data privacy, computational resource allocation, and clinical workflow integration are addressed through a federated learning approach. Results from a simulated hospital environment comprising 1,250 device nodes and 500 patient monitoring channels validate the system's efficacy, showing a 31.6\% improvement in mean time between failures for critical equipment and a 42.7\% enhancement in clinically significant event detection. This research provides a robust technological foundation for next-generation intelligent healthcare infrastructure.