Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models.

ABSTRACT: The triage process in emergency departments (EDs) relies on the subjective assessment of medical practitioners, making it unreliable in certain aspects. There is a need for a more accurate and objective algorithm to determine the urgency of patients. This paper explores the application of advanced data-synthesis algorithms, machine learning (ML) algorithms, and ensemble models to predict patient mortality. Patients predicted to be at risk of mortality are in a highly critical condition, signifying an urgent need for immediate medical intervention. This paper aims to determine the most effective method for predicting mortality by enhancing the F1 score while maintaining high area under the receiver operating characteristic curve (AUC) score. This study used a dataset of 7325 patients who vi

SUBMITTER: Son B 

PROVIDER: S-EPMC10497596 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature