Using machine learning to predict and treat circulatory shock in critically ill patients
Developing Machine Learning-Driven Prediction Models and Therapeutic Strategies for Circulatory Shock in Critically-ill Patients
This study is working on using smart computer technology to help doctors spot early signs of circulatory shock in seriously ill patients, so they can provide better, personalized care based on data from healthy blood donors.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project ID | NIH-10887419 on NIH RePORTER |
What this research studies
This research focuses on developing advanced machine learning models to predict and manage circulatory shock in critically ill patients. By analyzing non-invasive waveform data from healthy blood donors, the research aims to create individualized prediction models that can identify early signs of shock and guide personalized treatment strategies. The principal investigator, a pulmonary and critical care physician, will also enhance their skills in clinical informatics and bioinformatics to support this innovative approach. The ultimate goal is to improve patient outcomes in intensive care units through data-driven methodologies.
Who could benefit from this research
Good fit: Ideal candidates for this research are critically ill patients who are at risk of developing circulatory shock.
Not a fit: Patients who are not critically ill or do not have conditions leading to circulatory shock may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions and personalized treatments for patients experiencing circulatory shock, potentially saving lives.
How similar studies have performed: Other research has shown promise in using machine learning for predictive modeling in critical care, suggesting that this approach could be effective.
Where this research is happening
Pittsburgh, United States
- University of Pittsburgh at Pittsburgh — Pittsburgh, United States (Active)
Researchers
- Principal investigator: Yoon, Joo Heung — University of Pittsburgh at Pittsburgh
- Study coordinator: Yoon, Joo Heung
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 below.