Risk adjustment models need further improvement in their quality through the inclusion of a parsimonious set of clinically relevant variables, appropriately handling missing values and model validation, and utilising machine learning methods.
Keyphrases
- percutaneous coronary intervention
- machine learning
- st segment elevation myocardial infarction
- acute coronary syndrome
- coronary artery disease
- acute myocardial infarction
- cardiovascular events
- st elevation myocardial infarction
- antiplatelet therapy
- coronary artery bypass grafting
- patients undergoing
- artificial intelligence
- atrial fibrillation
- risk factors
- big data
- quality improvement
- deep learning
- type diabetes
- coronary artery bypass
- left ventricular