About this document
Predicting Long ED Stays with Imbalanced Learning by matheustaci10 is a document available to read on EtoBox.
This paper presents a predictive framework utilizing imbalanced learning techniques to identify emergency department patients likely to have prolonged lengths of stay (LOS) exceeding 14 hours. By analyzing over 100,000 patient encounters, the framework combines patient demographics, clinical data, and ED resource metrics to improve prediction accuracy and resource management. The study demonstrates that integrating class imbalance learning methods significantly enhances the prediction of prolonged ED stays
- Author
- matheustaci10
- Language
- EN