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- W4313137841 abstract "Myocardial infarction is a serious risk for patients with cardiovascular-related diseases. This study aims to predict the mortality in myocardial infarction patients during hospitalization. We used the Mimic III database from 2001 to 2012 to build the learning models. Four sampling methods, Over-Sampling, Under-Sampling, KMeansSMOTE, and Repeated Edited Nearest Neighbors were used for data scaling in the experiments. A variety of classification algorithms and feature analysis tools such as SHAP were adopted to adjust the suitable model. After a series of experiments, we obtain 91.3% accuracy based on the XGBoost model with different settings and processes on the dataset." @default.
- W4313137841 created "2023-01-06" @default.
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- W4313137841 date "2022-05-27" @default.
- W4313137841 modified "2023-09-27" @default.
- W4313137841 title "Using Deep Learning to Predict Mortality in Myocardial Infarction Patients" @default.
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- W4313137841 doi "https://doi.org/10.1109/ecbios54627.2022.9944990" @default.
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