Do machine learning algorithms provide better discrimination than conventional statistical models for predicting readmission and mortality in heart failure patients?
Machine learning algorithms demonstrate better discrimination than conventional statistical models for predicting heart failure readmission and mortality, though external validation remains a critical need.
ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML-based studies of prediction modelling. We suggest that ML-based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867.
Shin et al. (Tue,) studied this question.