Cui Hongzhen, Zhu Xiaoyue, Zhang Wei, Piao Meihua, Peng Yunfeng
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China.
Front Cardiovasc Med. 2025 Apr 28;12:1526247. doi: 10.3389/fcvm.2025.1526247. eCollection 2025.
Cardiovascular diseases are diverse, intersecting, and characterized by multistage complexity. The growing demand for personalized diagnosis and treatment poses significant challenges to clinical diagnosis and pharmacotherapy, increasing potential medication risks for doctors and patients. The Cardiovascular Medication Guide (CMG) demonstrates distinct advantages in managing cardiovascular disease, serving as a critical reference for front-line doctors in prescription selection and treatment planning. However, most medical knowledge remains fragmented within written records, such as medical files, without a cohesive organizational structure, leading to an absence of clinical support from visualized expert knowledge systems.
This study aims to construct a comprehensive Expert Knowledge Graph of Cardiovascular Medication Guidelines (EKG-CMG) by integrating unstructured and semi-structured Cardiovascular Medication Knowledge (CMK), including clinical guidelines and expert consensus, to create a visually integrated cardiovascular expert knowledge system.
This study utilized consensus and guidelines from cardiovascular experts to organize and manage structured knowledge. BERT and knowledge extraction techniques capture drug attribute relationships, leading to the construction of the EKG-CMG with fine-grained information. The Neo4j graph database stores expert knowledge, visualizes knowledge structures and semantic relationships, and supports retrieval, discovery, and reasoning of knowledge about medication. A hierarchical-weighted, multidimensional relational model to mine medication relationships through reverse reasoning. Experts participated in an iterative review process, allowing targeted refinement of expert medication knowledge reasoning.
We construct an ontology encompassing 12 cardiovascular "medication types" and their "attributes of medication types". Approximately 15,000 entity-relationships include 22,475 medication entities, 2,027 entity categories, and 3,304 relationships. Taking beta-blockers (β) as an example demonstrates the complete process of ontology to knowledge graph construction and application, encompassing 41 AMTs, 1,197 entity nodes, and 1,351 relationships. The EKG-CMG can complete knowledge retrieval and discovery linked to "one drug for multiple uses," "combination therapy," and "precision medication." Additionally, we analyzed the knowledge reasoning case of cross-symptoms and complex medication for complications.
The EKG-CMG systematically organizes CMK, effectively addressing the "knowledge island" issues between diseases and drugs. Knowledge potential relationships have been exposed by leveraging EKG-CMG visualization technology, which can facilitate medication semantic retrieval and the exploration and reasoning of complex knowledge relationships.
心血管疾病多种多样、相互交叉，具有多阶段复杂性。对个性化诊断和治疗的需求不断增加，给临床诊断和药物治疗带来了重大挑战，增加了医生和患者的潜在用药风险。《心血管药物指南》（CMG）在心血管疾病管理中显示出明显优势，是一线医生处方选择和治疗规划的重要参考。然而，大多数医学知识仍分散在书面记录中，如病历，缺乏连贯的组织结构，导致可视化专家知识系统缺乏临床支持。
本研究旨在通过整合非结构化和半结构化的心血管药物知识（CMK），包括临床指南和专家共识，构建一个全面的心血管药物指南专家知识图谱（EKG-CMG），以创建一个可视化的综合心血管专家知识系统。
本研究利用心血管专家的共识和指南来组织和管理结构化知识。BERT和知识提取技术捕捉药物属性关系，从而构建具有细粒度信息的EKG-CMG。Neo4j图数据库存储专家知识，可视化知识结构和语义关系，并支持药物知识的检索、发现和推理。通过反向推理的分层加权多维关系模型来挖掘药物关系。专家参与迭代审查过程，从而有针对性地完善专家用药知识推理。
我们构建了一个包含12种心血管“药物类型”及其“药物类型属性”的本体。大约15,000个实体关系包括22,475个药物实体、2,027个实体类别和3,304个关系。以β受体阻滞剂（β）为例，展示了从本体到知识图谱构建和应用的完整过程，包括41种药物治疗适应症（AMT）、1,197个实体节点和1,351个关系。EKG-CMG可以完成与“一药多用”、“联合治疗”和“精准用药”相关的知识检索和发现。此外，我们分析了交叉症状和并发症复杂用药的知识推理案例。
EKG-CMG系统地组织了CMK，有效解决了疾病和药物之间的“知识孤岛”问题。通过利用EKG-CMG可视化技术，知识潜在关系得以揭示，这有助于药物语义检索以及复杂知识关系的探索和推理。
