MedRDSyn: Knowledge Graph-Based Rare Disease Data Synthesis for Supervised Fine-Tuning

To address the scarcity of real clinical records for rare diseases, we propose MedRDSyn, a knowledge-graph–guided framework for rare disease clinical note synthesis. MedRDSyn curates rare disease notes from MIMIC-IV-Note as clinical record templates and defines entity types and relation types required by clinical records. Through LLM-assisted incremental triple extraction from PubMed, MedRDSyn integrates Orphadata, HPO, and DrugBank, while using UMLS for entity normalization to construct a rare disease knowledge graph. The graph explicitly models associations among key medical entities, including genes, variants, proteins, diseases, and phenotypes, and contains 48,379 entities and 654,814 triples. Based on this knowledge graph, MedRDSyn performs LLM consistency verification with source-confidence fusion to construct a weighted graph, estimates node importance via weighted PageRank, and retrieves informative subgraphs through temperature-controlled seed sampling and beam-search expansion. After quality filtering, the framework generates 14,035 high-quality synthetic rare disease clinical cases and chain-of-thought data. Supervised fine-tuning on Qwen3-0.6B, Qwen3-4B, and Qwen3-8B using the synthesized data yields consistent improvements on PubMedQA, MedQA, ReDisQA, and RareBench, demonstrating the effectiveness of graph-guided rare disease data synthesis.