Cost-Sensitive Deep Learning for Predicting Hospital Readmission: Enhancing Patient Care and Resource Allocation
Keywords:
Cost-sensitive learning, deep learning, hospital readmission prediction, healthcare analytics, resource allocation, predictive modeling.Abstract
Predicting hospital readmission is crucial for improving patient care and optimizing healthcare
resource allocation. Traditional methods often overlook the imbalanced costs associated with
different types of prediction errors. This study proposes a cost-sensitive deep learning approach
tailored for hospital readmission prediction. By integrating cost-sensitive learning techniques into
deep neural networks, the model prioritizes minimizing costly prediction errors, such as false
negatives. Experimental results on a large-scale healthcare dataset demonstrate the effectiveness
of the proposed approach in achieving higher predictive accuracy while mitigating readmission
risks. The findings highlight the potential of cost-sensitive deep learning to enhance healthcare
outcomes and resource utilization.
