Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data.

In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unstructured data, which contains valuable patient information such as symptoms and medical histories, often presents challenges, including lengthy text sequences and incomplete data. To address these issues, we introduce a new framework named Hierarchical Attention-based Integrated Learning (HAIL), designed to predict in-hospital mortality and the duration of stay in the intensive care unit. HAIL combines hierarchical attention mechanisms with graph neural networks to effectively manage missing data and enhance outcome predictions. Our model iteratively refines embeddings, resulting in a more thorough analysis of electronic health record data. Experimental findings demonstrate a notable performance improvement of 2%-3% across various metrics when compared to existing benchmarks on standard datasets, highlighting HAIL's effectiveness in time-sensitive clinical decision-making. Additionally, our analysis underscores the significance of patient networks in maintaining the robustness and consistent performance of the HAIL framework.