Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response.
Sheikh Shah Mohammad Motiur RahmanZhihao ChenAlain LalandeThomas DecourselleAlexandre CochetThibaut PommierYves CottinMichel SalomonRaphaël CouturierPublished in: PloS one (2023)
Our study provides a reliable approach to classify the patients in emergency department between myocarditis, myocardial infarction or other patient condition from only clinical information, considering DE-MRI as ground-truth. Among the different machine learning and ensemble techniques tested, the stacked generalization technique is the best one providing an accuracy of 97.4%. This automatic classification could provide a quick answer before imaging exam such as cardiovascular MRI depending on the patient's condition.
Keyphrases
- machine learning
- deep learning
- emergency department
- end stage renal disease
- big data
- magnetic resonance imaging
- artificial intelligence
- case report
- contrast enhanced
- heart failure
- ejection fraction
- chronic kidney disease
- newly diagnosed
- convolutional neural network
- high resolution
- left ventricular
- peritoneal dialysis
- prognostic factors
- diffusion weighted imaging
- neural network
- magnetic resonance
- healthcare
- patient reported outcomes
- mass spectrometry
- atrial fibrillation