A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department.

ABSTRACT: Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58-83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing ca

SUBMITTER: Dipaola F 

PROVIDER: S-EPMC10817569 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature