Research in stroke risk prediction is enhanced by the inclusion of a broad range of data from different patient cohorts that strengthen the evidence for the scientific questions being investigated. In two recent AI Health Virtual Seminars, researchers from Duke AI Health and the American Heart Association (AHA) introduced an open metadata repository designed to give researchers access to the processes and methods used to harmonize data from four large, National Institutes of Health-funded cohort studies.
In the first seminar, presenters explained how the datasets were harmonized and demonstrated how users can access and utilize the metadata repository. In a follow-up seminar, leading researchers presented new methodologies and results from studies that were conducted with the harmonized dataset. The repository offers resources and tools for studies in stroke risk prediction, and researchers are invited to review the seminar recordings to learn more about how they can leverage it for their own work.
Watch the recordings to learn how the metadata repository supports research into stroke risk prediction:
- Part 1: Facilitating Harmonization of Variables from the Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository
- Part 2: Research Applications with Harmonized Variables from the Framingham, MESA, ARIC, and REGARDS Studies
