In their article, Wändell et al. (2025) developed and validated a machine learning (ML) model to predict short-term mortality among patients presenting to the emergency department (ED) with diabetes or hyperglycemia. This retrospective cohort study analyzed ED encounters between 2017-2018 in Scania, Sweden, with prediction of 30 day mortality as the primary outcome. Model training incorporated demographic and key physiological variables, including arterial blood gas values, pulmonary function measures, and urine analyses. Among men aged 40-69 years old, the ML model achieved 92% sensitivity, 94% specificity, and 94% overall accuracy for predicting 30 day mortality. Comparable performance was observed in women aged 40-69, with modestly lower accuracy in both women and men older than 70. Overall, the model demonstrated high reliability in identifying low-risk patients, though it tended to overpredict mortality in patients among high risk groups. Wändell et al. suggest that given the model's strong negative predicting value, the model could aid clinicians in safely expediting discharges and optimizing ED resource allocation.