Data for Stroke Care
Supporting Stroke Care with Multimodal Data
We aim to develop data science methods that enable real-world evidence for stroke care, enabling better treatment decisions and outcomes.
Transforming Stroke Care Through Data Science and Real-World Evidence
Problem and Need for the Study
Acute ischemic stroke (AIS) is a dangerous yet prevalent brain condition that can lead to instant death or chronic disability. Due to the brain’s complexity, there is a critical need to supplement data from randomized controlled trials with real-world EHR-based stroke research. This approach can accelerate the investigation of patient responses to treatments under various disease conditions, addressing an unmet need in acute ischemic stroke treatment.
Innovation and Impact
To inform treatment decisions for AIS, we propose developing an evidence-based precision medicine framework by mining the electronic health records (EHRs) at M Health Fairview. The overall goal is to use EHR-based stroke research to support stroke care with real-world evidence.
Our aims are to develop data science methods that:
- Curate functional outcomes data from EHRs
- Derive imaging markers that link damaged and salvageable locations in the brain to functional outcomes
- Assess treatment effect while adjusting for background temporal shifts in patient outcomes
Key Personnel
Performance Sites
University of Minnesota
- Multiple Principal Investigators: Jue Hou, Margy McCullough-Hicks, Rui Zhang, Ju Sun, Christopher Streib
Grant Details
- The Data Science Initiative Seed Grant is a 1 year, $111,615 award.
- Project dates: 01-January-2024 to 31-December-2024