A study compared nine machine learning models to predict mortality in patients admitted to the emergency department, analyzing data from 1,389 patients from 2021[1]. The LightGBM model achieved the best results with an accuracy of 96.05%, a sensitivity of 78.12% and a specificity of 93.90%[1]. Among the most important predictors of death were serum lactate, Glasgow Coma Scale, albumin, base excess and systolic blood pressure[1]. Calibration curves confirmed that the predicted values matched the actual observed death rates, confirming the reliability of the model[1]. Stratification of patients according to predicted risk effectively divided them into groups with different prognoses[1]. The researchers conclude that machine learning models, especially LightGBM, make it possible to accurately predict the risk of death and can help doctors make timely decisions about treatment and the allocation of medical resources[1].