Why the study?
Predicting the prognosis of patients with acute myocardial infarction combined with diabetes mellitus is crucial due to high in-hospital mortality rates.
Does a Random Forest machine learning model accurately predict in-hospital mortality in patients with acute myocardial infarction combined with diabetes mellitus?
Population
Patients with AMI combined with DM from MIMIC-IV
Comparison
Seven machine learning algorithms to predict in-hospital mortality
Design
Retrospective database prediction model development and validation study
Follow-up
During hospitalization
Authors
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May support ML mortality prediction in AMI-diabetes; hypothesis-generating pending external validation before clinical use.
Does a Random Forest machine learning model accurately predict in-hospital mortality in patients with acute myocardial infarction combined with diabetes mellitus?
A Random Forest machine learning model, enhanced by SHAP and LIME for interpretability, can effectively predict the risk of in-hospital mortality in patients with AMI and diabetes.
Bu et al. (2025) studied this question.
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