Related Experiment Video
Updated: May 15, 2025
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
From knowledge silos to integrated insights: building a cardiovascular medication knowledge graph for enhanced
Hongzhen Cui1, Xiaoyue Zhu1, Wei Zhang2,3
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Insights
This study constructs an Expert Knowledge Graph of Cardiovascular Medication Guidelines (EKG-CMG) to integrate fragmented knowledge, improving personalized cardiovascular disease treatment and reducing medication risks for better clinical decision-making.
Area of Science:
- Cardiology
- Medical Informatics
- Knowledge Engineering
Background:
- Cardiovascular diseases present complex diagnostic and treatment challenges, increasing medication risks.
- Existing cardiovascular medication knowledge is fragmented, hindering clinical decision support.
- Personalized medicine demands integrated, visualized expert knowledge systems.
Purpose of the Study:
- To construct a comprehensive Expert Knowledge Graph of Cardiovascular Medication Guidelines (EKG-CMG).
- To integrate unstructured and semi-structured cardiovascular medication knowledge (CMK).
- To create a visually integrated cardiovascular expert knowledge system for clinical support.
Main Methods:
- Utilized expert consensus and guidelines to structure knowledge.
- Employed BERT and knowledge extraction for drug attribute relationships.
- Stored and visualized knowledge in Neo4j graph database for retrieval and reasoning.
Main Results:
- Constructed an ontology with 12 medication types and detailed attributes.
- Developed EKG-CMG with 22,475 medication entities and 3,304 relationships.
- Demonstrated EKG-CMG's capability for knowledge retrieval in precision medication scenarios.
Conclusions:
- EKG-CMG systematically organizes CMK, bridging knowledge gaps between diseases and drugs.
- Visualization technology facilitates semantic retrieval and complex knowledge relationship exploration.
- The system supports enhanced medication semantic retrieval and reasoning for clinical application.
Background:
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.
Purpose:
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.
Methods:
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.
Results:
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.
Conclusion:
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.
More Related Videos
13:18Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
Published on: March 3, 2023
07:51Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
Related Concept Videos
01:18Cardiovascular Drugs: Classification based on Therapeutic Indications
01:28Structure-Activity Relationships and Drug Design
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
01:21Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs
On the other hand, integral calculus focuses on...
01:22Heart Failure Drugs: β-Blockers
01:26Drug Discovery: Overview
01:20Pharmacokinetic Models: Overview
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...