KGSD-Net: a knowledge graph syndrome differentiation network for syndrome classification.

The integration of electronic medical records (EMRs) in modern healthcare holds significant promise; however, traditional approaches to syndrome differentiation in Traditional Chinese Medicine (TCM) often encounter limitations due to incomplete data and inconsistent frameworks. This paper addresses these challenges by introducing a novel methodology that employs large-scale language models (LLMs) to extract relevant entities from an semi-structured TCM knowledge base, facilitating the construction of a dynamic TCM knowledge graph. By applying the DeepWalk method for latent knowledge graph embedding, hidden patterns essential for accurate diagnosis are uncovered. Furthermore, a combined entity linking approach is implemented to align this knowledge graph with diagnostic data extracted from EMRs, enhancing clinicians' insights through essential knowledge-based embeddings tailored specifically for syndrome differentiation tasks. Additionally, the integration of the BERT model with knowledge graph embedding technologies strengthens dialectical reasoning within TCM practice and demonstrates superior performance on specialized datasets compared to prior methodologies.