A machine learning model using Stochastic Gradient Descent predicted in-hospital mortality in acute myocardial infarction patients with an AUC of 88% when incorporating laboratory and physiological data.
Observational (n=1,749)
No
Can machine learning models using clinical and laboratory data accurately predict mortality in patients admitted with acute myocardial infarction?
Machine learning models incorporating laboratory and clinical data can achieve high discriminatory capacity (AUC 88%) for predicting mortality in patients with acute myocardial infarction.
Effect estimate: AUC 0.88
BACKGROUND: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. METHODS: Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. RESULTS: Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. CONCLUSIONS: Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.
Oliveira et al. (Tue,) conducted a observational in Acute Myocardial Infarction (n=1,749). Machine learning predictive models was evaluated on In-hospital mortality prediction (AUC) (AUC 0.88). A machine learning model using Stochastic Gradient Descent predicted in-hospital mortality in acute myocardial infarction patients with an AUC of 88% when incorporating laboratory and physiological data.