Predicting Acute Clinical Deterioration With Interpretable Machine Learning To Support Emergency Care Decision Making

This study aims to compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR). The study uses a dataset comprising 118,886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. The results show up to a 0.366 increase in average precision, up to a reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex.