Empirically, we verify the computational efficiency of our methods.

In this paper, we address this gap by dividing a typical patient's clinical journey

Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.

Typically, preference optimization is approached as an offline supervised learning task using manually crafted convex loss functions. While these methods are based on theoretical insights, they are inherently constrained by human creativity, so the large search space of possible loss functions remains under-explored.

These limitations hinder GNNs' ability to capture long-range dependencies in graphs, ultimately