Ai Algorithms For Predicting Cardiovascular Events Based On Electronic Medical Records

Abstract

Cardiovascular diseases (CVD) remain the leading cause of death and disability in the world, which underscores the critical need for effective primary and secondary prevention. Traditional risk assessment scales (Framingham, ASCVD, SCORE/SCORE2) based on a limited set of clinical indicators demonstrate insufficient predictive accuracy, especially in middle-aged patients without obvious symptoms. With the development of digital technologies, electronic medical records (EMRs) have become a valuable source of multidimensional, long-term data, opening up opportunities for the use of artificial intelligence (AI) and machine learning (ML) methods in predicting acute cardiovascular events such as myocardial infarction, ischemic stroke and hospitalization for heart failure.

This review analyzes current research (2015-2025) in which AI models were trained on real-world EMR data to predict specific cardiovascular outcomes. Both technical aspects (types of algorithms, processing of structured and unstructured data, consideration of time dynamics) and key issues of clinical applicability are considered: interpretability, external validation, resistance to algorithmic bias and integration into the daily work of a doctor. Particular attention is paid to the risk of overfitting, biases in data, and the ethical consequences of uncritical AI adoption.

The analysis shows that AI really improves prognostic accuracy compared to traditional approaches and makes it possible to identify patients from the "gray area" overlooked by standard methods. However, the real clinical benefit does not depend on the complexity of the model, but on its ability to be transparent, calibrated, fair, and seamlessly integrated into the clinical workflow. Successful implementation of AI in practice requires interdisciplinary collaboration, rigorous validation, and a clear understanding that AI is not a substitute for a doctor, but a decision—making support tool.