Interpretable machine learning for in-hospital mortality risk prediction in patients with ST-elevation myocardial infarction after percutaneous coronary interventions.

Background And Objective: Despite the constant improvement of coronary heart disease (CHD) diagnostics and treatment methods it remains one of the main causes of death in most countries around the world. And myocardial infarction with ST segment elevation on the electrocardiogram (STEMI) still is one of the most dangerous clinical variants of CHD. This study aims to develop an explainable machine learning model for in-hospital mortality (IHM) risk prediction in STEMI patients after myocardial revascularization by percutaneous coronary intervention (PCI).

[Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting].

Aim      To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery.Material and methods  A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008-2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed.