CARDIO-TWIN: Cardiovascular Risk Detection among Women using Digital Twins

Status Ongoing
Start date 2024
Location Quebec
Researcher Rahimi, Samira
Associated institution McGill University
Summary

Cardiovascular diseases are the leading cause of death, and women are especially affected due to delayed diagnoses and gaps in risk assessment. Existing prediction tools often fail to capture important factors such as family history, pregnancy‑related risks and social determinants of health. The CARDIO‑TWIN project aims to improve early detection by building machine‑learning models that identify adults at higher risk of cardiovascular disease. Using CARTaGENE and other population datasets, the models will classify individuals based on their health and lifestyle profiles. Access to CARTaGENE’s follow‑up data will help evaluate how risk changes over time. Separate models for women and men will better capture sex‑specific patterns. Ultimately, CARDIO‑TWIN seeks to support more accurate, inclusive and personalized prevention strategies for cardiovascular health.

Themes
  • Chronic diseases
  • Methodology and biostatistics
Data types
  • Linked data
  • Physical and cognitive measures
  • Questionnaire data