SCH: Transfer Regression to Enable Cross-Domain Cardiovascular Event Prediction

SCH: Transfer Regression to Enable Cross-Domain Cardiovascular Event Prediction This project proposes fundamental novelties (e.g., transfer volume regression) in computer science, data science, and biomedical engineering to address the critical health challenge of lacking a standardized clinical risk prediction for major cardiovascular events (CVe)—defined as stroke, heart attack, heart failure

(HF), and death. Our proposal includes a pioneering transfer regression learning method, combined with several novel machine learning and data science techniques, to develop the first smart, standardized, fairness-aware, and user-friendly CVe prediction model. This model leverages commonly used, low-cost

(sometimes free), safe, and quick screening programs (featuring low radiation and no need for contrast agents), aiming to significantly enhance clinical outcomes. The efficacy and robustness of this model are to be validated using four large and diverse datasets, exemplifying a significant advancement in Smart

Health and Biomedical Research in the era of Artificial Intelligence and Advanced Data Science (SCH). RELEVANCE (See instructions): We will develop and validate transfer volume regression to enable quantitative clinical risk predictions for major cardiovascular events (defined as stroke, heart attack, heart failure, and death in this proposal) to

guide preventive therapy.