Contributor Information

• Name: Joshua Steier
• Contribution Type: Core Infrastructure + Processor + Tests + Documentation

Description

Adds native knowledge graph support to PyHealth via a new pyhealth.graph module and a GraphProcessor in pyhealth.processors. This is foundational infrastructure enabling graph-based EHR models (GraphCare, G-BERT, KAME, etc.) to consume standard PyHealth datasets through the existing processor/schema pipeline.

Builds on Patrick Jiang's earlier BaseKGDataset work on the kg_embedding branch, updated for PyHealth 2.0's processor architecture.

What's included:

pyhealth.graph.KnowledgeGraph — Core data structure for medical knowledge graphs:

• Loads (head, relation, tail) triples from Python lists or CSV/TSV files
• Builds entity2id/relation2id mappings automatically
• Stores edges as PyG-compatible edge_index + edge_type tensors
• Provides k-hop subgraph() extraction via torch_geometric.utils.k_hop_subgraph
• Supports optional pre-computed node features (TransE, LLM embeddings, etc.)
• Neighbor lookup via BFS

pyhealth.processors.GraphProcessor — Registered processor bridging EHR codes to graphs:

• Takes patient medical codes → looks up in KG → returns PyG Data subgraph
• Handles flat code lists and multi-visit (list of list) code inputs
• Optional max_nodes pruning (seeds always kept)
• Implements FeatureProcessor interface: is_token(), schema(), dim(), spatial()

Collation update — Added PyG Data branch to collate_fn_dict_with_padding:

• Detects PyG Data objects and batches via Batch.from_data_list()
• torch-geometric is optional — gracefully skips when not installed
• Zero impact on existing non-graph pipelines

Usage

from pyhealth.graph import KnowledgeGraph

kg = KnowledgeGraph(triples="data/umls_triples.csv")

input_schema = {
    "conditions": ("graph", {
        "knowledge_graph": kg,
        "num_hops": 2,
        "max_nodes": 500,
    }),
}

Design decisions:

• torch-geometric is an optional dependency — imported only when graph features are used
• User provides the KG — PyHealth does not generate it (per discussion with @jhnwu3)
• Follows same patterns as TimeImageProcessor for registration and integration

Files to Review

New files:

• pyhealth/graph/__init__.py — module exports
• pyhealth/graph/knowledge_graph.py — KnowledgeGraph class
• pyhealth/processors/graph_processor.py — GraphProcessor
• tests/core/test_knowledge_graph.py — 22 unit tests
• tests/core/test_graph_processor.py — 19 unit tests
• docs/api/graph.rst — graph module docs
• docs/api/graph/pyhealth.graph.KnowledgeGraph.rst — KG API docs
• docs/api/processors/pyhealth.processors.GraphProcessor.rst — processor docs

Edited files:

• pyhealth/processors/__init__.py — added GraphProcessor import
• pyhealth/datasets/utils.py — added PyG collation branch
• docs/api/processors.rst — added to toctree
• docs/index.rst — added graph section

Testing

python -m unittest tests/core/test_knowledge_graph.py -v
# 22 tests

python -m unittest tests/core/test_graph_processor.py -v
# 19 tests (skip gracefully if torch-geometric not installed)

# Smoke tests
python pyhealth/graph/knowledge_graph.py
python pyhealth/processors/graph_processor.py

Next steps (separate PRs)

1. GraphCare model consuming this infrastructure
2. Pre-built KG artifacts for MIMIC (UMLS subgraphs, LLM-generated KGs)
3. Additional graph models (G-BERT, KAME)