Creating advanced models to predict complications during anesthesia
Synthesizing Intraoperative Multivariate Time Series with Conditional Generative Adversarial Networks
This study is working on a smart system that helps doctors keep you safe during surgery by predicting any problems that might happen while you're under anesthesia, using data from your body to spot potential risks and improve care.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project ID | NIH-10840481 on NIH RePORTER |
What this research studies
This research focuses on enhancing patient safety during anesthesia by predicting intraoperative complications and hemodynamic instability. It aims to develop a system that analyzes large amounts of physiological data collected during surgery to identify patterns that could indicate potential risks. By utilizing advanced mathematical tools and machine learning techniques, the project seeks to generate realistic intraoperative data that can help anesthesia providers make informed decisions before and during surgical procedures. Ultimately, this could lead to the creation of a real-time clinical decision support system to improve patient outcomes.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients undergoing surgical procedures that require anesthesia.
Not a fit: Patients who are not undergoing surgery or do not require anesthesia may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could significantly reduce the risk of complications during anesthesia, leading to improved patient safety and outcomes.
How similar studies have performed: Other research has shown promise in using machine learning for predicting surgical complications, indicating 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: Zhang, Fei — University of Pittsburgh at Pittsburgh
- Study coordinator: Zhang, Fei
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.