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.