Harmonizing Healthcare Perspectives and In-Depth Strategy for Unifying Diverse Medical Big Data Sources

The healthcare sector has generated vast amounts of data, posing a significant challenge in terms of integration. Recently, deep learning frameworks have gained extensive utilization for forecasting relationships within healthcare systems. Specifically, the success of the Heterogeneous Multi-Layer Deep Learning Network (HMLDN) in seamlessly amalgamating diverse medical data for enhanced diagnostics and storage is explored in this paper. The initial discussion delves into capsule optimized networks and attention maps, laying the groundwork for their application in the formation of HMLDN to achieve efficient data integration.