Summary of Machine Learning Predicts Long-term Mortality After Acute Myocardial Infarction Using Systolic Time Intervals and Routinely Collected Clinical Data, by Bijan Roudini et al.
Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data
by Bijan Roudini, Boshra Khajehpiri, Hamid Abrishami Moghaddam, Mohamad Forouzanfar
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates the performance of tree-based machine learning models in predicting long-term mortality in cardiac patients. The researchers used publicly available data from Taiwan and included demographic and clinical information, as well as two biomarkers: brachial pre-ejection period (bPEP) and brachial ejection time (bET). The advanced ensemble tree-based models – random forest, AdaBoost, and XGBoost – outperformed the baseline logistic regression model in predicting all-cause mortality within 14 years. Adding the biomarkers to the feature set further improved the models’ performance. This advancement may enable better treatment prioritization for high-risk individuals. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The study aims to predict long-term mortality in cardiac patients using machine learning models. Researchers use data from Taiwan and include demographic, clinical, and biomarker information. They compare the performance of tree-based models like random forest, AdaBoost, and XGBoost to a baseline model. The results show that these advanced models are better at predicting long-term mortality than the basic model. |
Keywords
* Artificial intelligence * Logistic regression * Machine learning * Random forest * Xgboost