Machine Learning Methods for Predicting Syncope Severity in the Emergency Department: A Retrospective Analysis

Syncope is a frequent reason for hospital emergency admissions, presenting significant challenges in determining its cause and associated risks. Despite its prevalence, research on using artificial intelligence (AI) to improve patient outcomes in this context has been limited. The main objective of current study is to predict the severity of syncope cases using machine learning (ML) algorithms based on data collected during on-site treatment and ambulance transportation.

This study analyzed 572 records from five Spanish public hospitals (2018-2021), focusing on hospitalization, ICU admission, and mortality. A three-phase strategy was used: data preprocessing, model exploration, and model selection. In the exploration phase, three data transformations techniques were applied and in each of them, models were evaluated using stratified 10-fold cross-validation, optimizing AUC, accuracy, and recall, with emphasis on minimizing false negatives (FN). The top-performing models were fine-tuned and tested. The strategy was implemented using Python libraries and a diverse set of ML classifiers were applied, including linear discriminant analysis (LDA), random forest (RF), dummy classifier (DC), and gradient boosting (GB).

The RF classifier performed best for predicting hospitalization, reducing FN to 37% and achieving a true negative rate (TN) of 78%, with a recall of 0.63 and accuracy of 0.74. For ICU, DC showed FN = 29%, TN = 57%, recall = 0.625, and accuracy = 0.58. The LDA classifier excelled in predicting hospital mortality, with FN = 40%, TN = 89%, recall = 0.6, and accuracy = 0.88. These results indicate that RF was superior for predicting hospitalization, while DC for ICU and LDA performed better for predicting mortality.

This study provides an experimental foundation for the application of ML techniques in managing syncope in ED. The intention is to stimulate AI research in this area, with a view to integrating these models into clinical workflows in the future.