Using machine learning to predict death and recurrence after ischemic stroke
Official title: Machine Learning Prediction of 1-Year Mortality and Recurrence after Ischemic Stroke Using Enriched EHR data
Funded at Pennsylvania State Univ Hershey Med Ctr · NIH-10904746 · Data updated 2026-11-12
This study is looking to help doctors better predict how likely it is for patients to face serious health issues or pass away within a year after having an ischemic stroke, using advanced technology to analyze health records, so they can provide better care and support for those at higher risk.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Pennsylvania State Univ Hershey Med Ctr NIH-funded |
| Lab location | 1 site (Hershey, United States) |
| Project start | August 11, 2023 |
| Project end | July 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $630,507 |
| NIH project number | 5R01NS128986-02 |
| Project ID | NIH-10904746 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals who have experienced an ischemic stroke and are at risk for mortality or recurrence within one year.
✕Less likely to help
Patients who have not experienced an ischemic stroke or those with other unrelated health conditions may not benefit from this research.
What this research studies
This research aims to improve predictions of 1-year mortality and recurrence rates following ischemic stroke by utilizing advanced machine learning techniques on enriched electronic health record (EHR) data. The approach involves analyzing a wide range of clinical variables to identify patients at higher risk for adverse outcomes. By developing more accurate predictive models, the research seeks to enhance patient care through timely interventions and resource allocation. The study will expand upon existing models to ensure broader applicability across different health systems.
Why it matters
Potential benefit: If successful, this research could lead to better risk assessment and management strategies for stroke patients, potentially reducing mortality and recurrence rates.
How similar studies have gone: Previous research has shown promise in using machine learning for predictive modeling in healthcare, indicating potential for success in this novel approach.
Where this research is happening
-
Pennsylvania State Univ Hershey Med Ctr
Hershey, United States Active
Researchers
- Principal investigator: Abedi, Vida — Pennsylvania State Univ Hershey Med Ctr
- Study coordinator: Abedi, Vida
Related research
Other NIH-funded projects on the same topics.
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Predicting 1-year death and recurrence after ischemic stroke using enriched EHR data
Researchers are using advanced computer models to identify which stroke patients are at higher risk of dying or having another stroke within a year, by analyzing enriched electronic health records.
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AI prediction of one-year death and repeat stroke after ischemic stroke
This project uses artificial intelligence and medical record data to predict whether people who had an ischemic stroke will die or have another stroke within one year.
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Improving blood flow after stroke by breaking down NETs
Aims to see if breaking down neutrophil extracellular traps (NETs) after an ischemic stroke can improve blood flow and reduce brain injury in patients.
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EP2 blockers to protect the brain after a stroke
Aims to test brain-permeable EP2 antagonists to reduce damage after ischemic stroke and improve recovery.
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Genetic testing to find causes of early-onset ischemic stroke
This project uses whole-exome sequencing to look for gene changes that may cause ischemic stroke in adults who had a stroke at a young age.
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Targeted nanomedicine to protect the brain after stroke
A targeted drug-delivery approach aims to boost blood-brain barrier repair and reduce brain damage after an acute ischemic stroke in patients at risk.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.