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, 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: England Springer Science and Business Media LLC 28.01.2021; BioMed Central; 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; Integrated approach; Interoperability; Medical screening; Natural Language Processing; Neural networks; Patients; Physical examinations; R858-859.7; Reliability engineering; Reproducibility of Results; Research projects; Risk analysis; Risk factors; Standardization; Tables (data); Thoracic surgery; Unstructured data; Veins & arteries; Database; Cardiovascular diseases; Artificial intelligence; Electronic health records

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 approa...