From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoning
BackgroundCardiovascular 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....
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-04-01
|
| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1526247/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849310429681025024 |
|---|---|
| author | Hongzhen Cui Xiaoyue Zhu Wei Zhang Wei Zhang Meihua Piao Yunfeng Peng |
| author_facet | Hongzhen Cui Xiaoyue Zhu Wei Zhang Wei Zhang Meihua Piao Yunfeng Peng |
| author_sort | Hongzhen Cui |
| collection | DOAJ |
| description | BackgroundCardiovascular 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.PurposeThis 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.MethodsThis 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.ResultsWe 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.ConclusionThe 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. |
| format | Article |
| id | doaj-art-8d6917ab95c442ce8dbdd93f31f448d2 |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-8d6917ab95c442ce8dbdd93f31f448d22025-08-20T03:53:43ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-04-011210.3389/fcvm.2025.15262471526247From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoningHongzhen Cui0Xiaoyue Zhu1Wei Zhang2Wei Zhang3Meihua Piao4Yunfeng Peng5School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaKey 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, ChinaShandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, ChinaSchool of Nursing, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, ChinaBackgroundCardiovascular 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.PurposeThis 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.MethodsThis 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.ResultsWe 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1526247/fullcardiovascular medication knowledgeexpert knowledge graphmultidimensional relationshipsontology constructionknowledge discover and reasoningEKG-CMG |
| spellingShingle | Hongzhen Cui Xiaoyue Zhu Wei Zhang Wei Zhang Meihua Piao Yunfeng Peng From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoning Frontiers in Cardiovascular Medicine cardiovascular medication knowledge expert knowledge graph multidimensional relationships ontology construction knowledge discover and reasoning EKG-CMG |
| title | From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoning |
| title_full | From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoning |
| title_fullStr | From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoning |
| title_full_unstemmed | From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoning |
| title_short | From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval, relationship discovery, and reasoning |
| title_sort | from knowledge silos to integrated insights building a cardiovascular medication knowledge graph for enhanced medication knowledge retrieval relationship discovery and reasoning |
| topic | cardiovascular medication knowledge expert knowledge graph multidimensional relationships ontology construction knowledge discover and reasoning EKG-CMG |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1526247/full |
| work_keys_str_mv | AT hongzhencui fromknowledgesilostointegratedinsightsbuildingacardiovascularmedicationknowledgegraphforenhancedmedicationknowledgeretrievalrelationshipdiscoveryandreasoning AT xiaoyuezhu fromknowledgesilostointegratedinsightsbuildingacardiovascularmedicationknowledgegraphforenhancedmedicationknowledgeretrievalrelationshipdiscoveryandreasoning AT weizhang fromknowledgesilostointegratedinsightsbuildingacardiovascularmedicationknowledgegraphforenhancedmedicationknowledgeretrievalrelationshipdiscoveryandreasoning AT weizhang fromknowledgesilostointegratedinsightsbuildingacardiovascularmedicationknowledgegraphforenhancedmedicationknowledgeretrievalrelationshipdiscoveryandreasoning AT meihuapiao fromknowledgesilostointegratedinsightsbuildingacardiovascularmedicationknowledgegraphforenhancedmedicationknowledgeretrievalrelationshipdiscoveryandreasoning AT yunfengpeng fromknowledgesilostointegratedinsightsbuildingacardiovascularmedicationknowledgegraphforenhancedmedicationknowledgeretrievalrelationshipdiscoveryandreasoning |