Cloud Computing and Deep Learning for Real-Time Anomaly Detection in Patient Monitoring Systems

Abstract

Real-time anomaly detection in patient monitoring systems is critical for timely intervention and improved patient outcomes. Traditional systems often rely on predefined thresholds and rules, which may not adequately capture complex physiological patterns or adapt to individual patient variability. Deep learning, enhanced by cloud computing, provides a robust framework for real-time anomaly detection by leveraging scalable computational resources and advanced data analysis capabilities. This paper explores the integration of cloud computing and deep learning for real-time anomaly detection in patient monitoring systems. We discuss various deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer models, and their applications in detecting anomalies in physiological data. We address challenges related to data security, latency, and system integration, and propose solutions for effective cloud deployment. By leveraging cloud computing, patient monitoring systems can achieve improved scalability, efficiency, and real-time responsiveness, enhancing patient care and safety.