Background: The purpose of this study was to identify the factors influencing the 90-day mortality of acute myocardial infarction(AMI) patients, and to establish a prognostic model for these patients based on the MIMIC-III database.Methods: Retrospective study methods were used to collect AMI patient data that met the inclusion criteria from the MIMIC-III database. Variable importance selection was determined using the random forest algorithm. Multiple logistic regression was used to determine

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... MI-related risk factors, with the results represented as a nomogram.Results: The baseline scores for the training and validation groups were very flat, and indicators for developing risk-model nomograms were obtained after random forest and multiple logistic regression. The AUC of the risk model was the highest (0.826 and 0.818 in the training and validation groups, respectively) . The Hosmer-Lemeshow goodness-of-fit test and standard curve both produced very consistent results. Both the NRI and IDI values indicated that the risk model had significant predictive power, and DCA results indicated that the risk model had good net benefits for clinical application.Conclusions: The results of this study indicated that age, troponinT, VT, VFI, MI_his, APS-III, bypass, and PCI were risk factors for 90-day mortality in AMI patients. Interactive nomograms could provide intuitive and concise personalized 90-day mortality predictions for AMI patients.