Zhang Guokai, Jiang Haoyu, Kuai Le, Li Bin, Huang Chenxi, Fei Xiaoya, Huang Zhiyuan
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Front Med (Lausanne). 2025 May 21;12:1555781. doi: 10.3389/fmed.2025.1555781. eCollection 2025.
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.
电子病历（EMR）在现代医疗保健中的整合具有重大前景；然而，由于数据不完整和框架不一致，中医传统的辨证方法往往存在局限性。本文通过引入一种新颖的方法来应对这些挑战，该方法利用大规模语言模型（LLM）从半结构化的中医知识库中提取相关实体，促进动态中医知识图谱的构建。通过应用深度行走（DeepWalk）方法进行潜在知识图谱嵌入，揭示了准确诊断所必需的隐藏模式。此外，实施了一种组合实体链接方法，使该知识图谱与从电子病历中提取的诊断数据对齐，通过专门为辨证任务定制的基于知识的基本嵌入增强临床医生的洞察力。此外，BERT模型与知识图谱嵌入技术的整合加强了中医实践中的辨证推理，并且在专门数据集上比先前的方法表现更优。
