Does machine learning modelling of irregularly repeated electronic health records improve cardiovascular risk prediction?
Machine learning models using irregular, repeated electronic health records can significantly improve cardiovascular risk prediction and the correct reclassification of high-risk patients.
Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.
Li et al. (Tue,) studied this question.