Spatiotemporal Analysis of Health Data using Graph Convolutional Networks in the Cloud

This study combines cloud computing infrastructure with graph convolutional networks (GCNs) to come up with a unique method for analyzing spatiotemporal health data. Based on an interpretive mindset, this study uses secondary data sources, including anonymized electronic health records geographic information, and a deductive method with a descriptive research design. The GCN model, which treats patients as nodes and their interactions as edges in a graph, is designed to capture complicated relationships within the spatiotemporal data. The model exhibits greater ability in spotting complex patterns, which include trends in patient migration and dynamics of illness propagation. The parallel processing capabilities of the cloud computing environment speed up the model training and assessment operations. The GCN outperforms conventional ways in handling complicated dependencies, as shown by comparison with existing techniques. Additionally, the implementation of cloud computing greatly improves computational effectiveness, allowing for the real-time analysis of massive datasets. The results of the study provided healthcare professionals and policymakers with insightful information. Planning for targeted interventions as well as resource allocation are influenced by changes in patient migration and disease transmission. The research emphasizes the significance of further investigation in this multidisciplinary domain by highlighting the potential for revolutionary influence on healthcare decision-making including resource allocation. The integration of cutting-edge analytical methods and cloud computing for better healthcare outcomes is emphasized in this study, which provides the groundwork for future research in spatiotemporal health data analysis.