Advancing Cardiovascular Risk Stratification with Machine Learning

Machine learning is increasingly recognized as a pivotal tool in the evolution of cardiovascular medicine, promising to refine risk prediction and support the delivery of personalized care. Traditional risk assessment models, like the Framingham Risk Score and similar clinical algorithms, primarily focus on a narrow range of variables and linear relationships, which limit their capacity to account for the complex causes underlying cardiovascular diseases. Recent studies have highlighted the limitations of these conventional models, especially in heterogeneous populations, where they often miss subtle genetic, behavioral, and environmental influences that drive disease onset and progression. The adoption of machine learning approaches offers a means to unlock and interpret vast, multifaceted datasets, including electronic health records, advanced imaging, genetic information, and continuous monitoring from wearable technology, addressing a critical gap in current practice. While early applications have demonstrated promising results, ongoing debates concern the reliability, generalizability, and transparency of these models, as well as the need for their rigorous validation before widespread clinical use.

This Research Topic aims to explore and advance the use of machine learning algorithms for cardiovascular disease risk assessment. Key objectives include identifying the limitations of existing prognostic tools, applying and comparing various machine learning approaches to large and diverse biomedical datasets, and determining which methodologies most accurately predict disease development across multiple cardiovascular conditions. Furthermore, the research will address crucial challenges such as the interpretability and transparency of models from both clinical and patient perspectives. Special attention will be paid to the ethical implications of increased algorithmic decision-making in medicine, focusing on issues of bias, data privacy, and the equitable distribution of potential benefits across diverse populations.

To gather further insights into the application of machine learning for cardiovascular risk prediction, we welcome articles that investigate both opportunities and limitations within this evolving field. Submissions may focus on, but are not limited to, the following themes:

o Development and validation of novel ML-based risk prediction models

o Integration of multi-modal data (e.g., clinical, imaging, genomics, wearables) in prognostic modeling

o Comparative performance of machine learning versus traditional risk assessment tools

o Strategies for enhancing model interpretability and clinician acceptance

o Ethical considerations, algorithmic bias, and regulatory challenges

o Real-world implementation of ML models in clinical cardiovascular practice.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review

Keywords: Machine learning, cardiovascular risk prediction, cardiovascular disease, predictive modeling, artificial intelligence, prognostics, risk stratification, electronic health records, deep learning, precision medicine, clinical decision support, model interp

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.