Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

Area of Science:

• Cardiovascular Medicine
• Biomolecular Sciences
• Computational Biology

Background:

• Cardiovascular diseases (CVD) cause millions of deaths globally, with recurrent events posing significant challenges.
• Recurrent cardiovascular events lead to high healthcare costs and necessitate personalized prevention strategies.
• Traditional risk factors are insufficient for precise prognosis, highlighting the need for advanced predictive tools.

Purpose of the Study:

• To review recent advancements in predicting recurrent cardiovascular events.
• To discuss the utility of multi-omics data and biomarkers in cardiovascular risk stratification.
• To explore the role of artificial intelligence (AI) in enhancing cardiovascular event prediction and clinical decision-making.

Main Methods:

• Review of current literature on cardiovascular disease risk prediction.
• Analysis of the integration of multi-omics data for a systems biology approach.
• Discussion of artificial intelligence (AI) and explainable AI (XAI) applications in cardiovascular medicine.

Main Results:

• Multi-omics data provide a holistic patient view, revealing novel insights into disease mechanisms.
• AI excels at identifying complex patterns in large datasets, improving risk prediction accuracy.
• Explainable AI (XAI) promises to enhance clinical decision support by increasing prediction transparency.

Conclusions:

• Integrating multi-omics data with AI holds significant potential for personalized cardiovascular risk prediction.
• Biomarkers derived from multi-omics analyses are key to understanding disease pathways.
• Explainable AI can bridge the gap between complex predictions and clinical practice, improving patient care.