Related Experiment Video
Updated: Nov 19, 2025
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases
Imjin Ahn1, Wonjun Na1, Osung Kwon2
1Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Insights
A new CardioNet database integrates electronic health records and unstructured data for artificial intelligence (AI) in cardiovascular disease (CVD) research. This resource aids early CVD prediction and personalized treatment strategies.
Area of Science:
- Biomedical Informatics
- Cardiology
- Artificial Intelligence
Background:
- Cardiovascular diseases (CVDs) pose diagnostic challenges, with easily overlooked risk factors.
- Artificial intelligence (AI) offers potential for early CVD prediction and personalized treatment.
- Specialized, curated databases from electronic health records (EHRs) are crucial for applying AI to CVD data.
Purpose of the Study:
- To develop CardioNet, a specialized database for cardiovascular diseases (CVDs).
- To enable AI-driven insights for improved CVD management and patient care.
- To facilitate multi-center research through standardized, interoperable data.
Main Methods:
- Collected anonymized EHR data from 748,474 patients with CVDs.
- Integrated structured EHR data with unstructured medical examination readings.
- Applied natural language processing to extract and structure key CVD variables.
- Standardized data using a common data model for multi-center research interoperability.
Main Results:
- Established CardioNet, a comprehensive database for AI model training in CVDs.
- The database includes 27 tables, a code-master, and a descriptive table for data utilization.
- CardioNet integrates EHR data and digital test readings for clinical management support.
Conclusions:
- The CardioNet database is established and specialized for cardiovascular diseases (CVDs).
- It supports ongoing multi-center research initiatives in CVD.
- CardioNet serves as a foundational resource for future CVD research and AI applications.
Background:
Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data.
Methods:
To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability.
Results:
CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table.
Conclusions:
CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.
Related Concept Videos
01:20Blood Studies for Cardiovascular System I: Cardiac Biomarkers
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
01:30Cardiomyopathy II: Dilated Cardiomyopathy
01:18Cardiovascular Drugs: Classification based on Therapeutic Indications
01:25Cardiomyopathy I: Introduction and Classification
01:30Coronary Artery Disease I: Introduction
01:18Neural Regulation of Blood Pressure
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...