Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation.
Osung KwonWonjun NaHee Jun KangTae Joon JunJihoon KweonGyung Min ParkYongHyun ChoCinyoung HurJungwoo ChaeSeung-Jung ParkPil Hyung LeeJung Min AhnDuck-Woo ParkSoo-Jin KangSeung Whan LeeCheol Whan LeeSeong-Wook ParkSeung Jung ParkDong-Hyun YangYoung Hak KimPublished in: JMIR medical informatics (2022)
Exploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.
Keyphrases
- machine learning
- big data
- healthcare
- left ventricular
- artificial intelligence
- coronary artery disease
- coronary artery
- electronic health record
- emergency department
- adverse drug
- deep learning
- heart failure
- aortic stenosis
- social media
- aortic valve
- health insurance
- smoking cessation
- transcatheter aortic valve replacement