Machine learning-based mortality risk prediction model for elderly diabetic patients with non-ST-segment elevation myocardial infarction using MIMIC-IV database.

ABSTRACT: Non-ST-elevation myocardial infarction (NSTEMI) in elderly diabetic patients presents unique challenges in risk assessment and prognosis prediction. This study aimed to develop and validate a machine learning-based mortality risk prediction model for this specific population using the MIMIC-IV database. We conducted a retrospective cohort study including 5,272 NSTEMI patients aged ≥ 55 years with diabetes from the MIMIC-IV database. Multiple machine learning models were developed using clinical data collected within 24 h of admission. The primary outcome was 28-day all-cause mortality. Model performance was evaluated using ROC curves, calibration plots, and decision curve analysis. SHAP analysis was employed to interpret model predictions. The XGBoost model demonstrated superior performanc

SUBMITTER: Song HL 

PROVIDER: S-EPMC12705734 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature