Do deep learning techniques perform better than traditional techniques in predicting 30-day readmissions in heart failure patients?
Deep learning models outperform traditional techniques in predicting 30-day readmissions for heart failure patients using EMR data, potentially enabling targeted interventions.
Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.
Golas et al. (Thu,) studied this question.