CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases

• Published in: BMC Medical Informatics and Decision Making Vol. 21; no. 1; pp. 29 - 15
• Main Authors: Ahn, Imjin, Na, Wonjun, Kwon, Osung, Yang, Dong Hyun, Park, Gyung-Min, Gwon, Hansle, Kang, Hee Jun, Jeong, Yeon Uk, Yoo, Jungsun, Kim, Yunha, Jun, Tae Joon, Kim, Young-Hak
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 28.01.2021; BioMed Central; Springer Nature B.V; BMC
• Subjects: Archives & records; Artificial Intelligence; Cardiac stress tests; Cardiology; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - diagnosis; Cardiovascular Diseases - epidemiology; Communications systems; Computer applications to medicine. Medical informatics; Coronary vessels; Data collection; Data processing; Databases, Factual; Deep learning; Electrocardiography; Electronic health records; Electronic medical records; Health care facilities; Health Informatics; Heart; Hospitals; Humans; Information Systems and Communication Service; Integrated approach; Interoperability; machine learning; Management of Computing and Information Systems; Medical screening; Medicine; Medicine & Public Health; modeling; Natural Language Processing; Neural networks; Patients; Physical examinations; R858-859.7; Reliability engineering; Reproducibility of Results; Research Article; Research projects; Risk analysis; Risk factors; Standardization; Tables (data); technology; Thoracic surgery; Unstructured data; Veins & arteries; Database; Cardiovascular diseases; Artificial intelligence; Electronic health records
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-021-01392-2

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.