SNOMED CT-powered Knowledge Graphs for Structured Clinical Data and Diagnostic Reasoning

• URL: http://arxiv.org/abs/2510.16899v1
• Date: Sun, 19 Oct 2025 15:50:33 GMT
• Title: SNOMED CT-powered Knowledge Graphs for Structured Clinical Data and Diagnostic Reasoning
• Authors: Dun Liu, Qin Pang, Guangai Liu, Hongyu Mou, Jipeng Fan, Yiming Miao, Pin-Han Ho, Limei Peng,
• Abstract summary: We present a knowledge-driven framework that integrates the standardized clinical terminology SNOMED CT with the Neo4j graph database to construct a structured medical knowledge graph.<n>By extracting and standardizing entity-relationship pairs, we generate structured,formatted datasets that embed explicit diagnostic pathways.<n> Experimental results demonstrate that our knowledge-guided approach enhances the validity and interpretability of AI-generated diagnostic reasoning.
• Score: 10.805834750887966
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The effectiveness of artificial intelligence (AI) in healthcare is significantly hindered by unstructured clinical documentation, which results in noisy, inconsistent, and logically fragmented training data. To address this challenge, we present a knowledge-driven framework that integrates the standardized clinical terminology SNOMED CT with the Neo4j graph database to construct a structured medical knowledge graph. In this graph, clinical entities such as diseases, symptoms, and medications are represented as nodes, and semantic relationships such as ``caused by,'' ``treats,'' and ``belongs to'' are modeled as edges in Neo4j, with types mapped from formal SNOMED CT relationship concepts (e.g., \texttt{Causative agent}, \texttt{Indicated for}). This design enables multi-hop reasoning and ensures terminological consistency. By extracting and standardizing entity-relationship pairs from clinical texts, we generate structured, JSON-formatted datasets that embed explicit diagnostic pathways. These datasets are used to fine-tune large language models (LLMs), significantly improving the clinical logic consistency of their outputs. Experimental results demonstrate that our knowledge-guided approach enhances the validity and interpretability of AI-generated diagnostic reasoning, providing a scalable solution for building reliable AI-assisted clinical systems.

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