Graph Neural Network Fundamentals for Medical Knowledge
Describe the core components and design choices in graph neural networks for modeling relational healthcare data.
Graph Neural Networks (GNNs) operate on data structured as nodes and edges, making them ideal for healthcare domains where relationships matter: drug-drug interactions, patient-provider networks, disease co-occurrence, or protein interactions. Key components include node features, edge features, aggregation functions, and update rules. GNNs learn by propagating information across the graph—each node integrates signals from its neighbors iteratively. This is fundamentally different from sequence or image models because medical knowledge is often inherently relational (drugs interact, diseases correlate, patients belong to populations).
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