Using machine learning to predict heart and breathing problems during surgery
Use of Machine Learning on Integrated Electronic Medical Record, Genetic andWaveform Data to Predict Perioperative Cardiorespiratory Instability
This study is looking at how to use computer technology to help doctors spot patients who might have heart or breathing problems during surgery, so they can take better care of them before, during, and after the procedure.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Icahn School of Medicine at Mount Sinai NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project ID | NIH-10906656 on NIH RePORTER |
What this research studies
This research focuses on developing machine learning techniques to analyze various healthcare data, including electronic medical records and genetic information, to predict cardiorespiratory instability during the perioperative period. The project aims to identify patients at risk for complications such as hypotension and arrhythmia before, during, and after surgery. Dr. Hofer, the principal investigator, is receiving training and mentorship to enhance his skills in applying these advanced analytical methods in a clinical setting. The research team includes experts in machine learning and perioperative medicine, ensuring a comprehensive approach to improving patient outcomes.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients scheduled for surgery who may be at risk for cardiorespiratory instability.
Not a fit: Patients who are not undergoing surgery or do not have conditions that could lead to cardiorespiratory instability may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to better prediction and management of heart and breathing issues during surgery, ultimately improving patient safety and outcomes.
How similar studies have performed: Other research has shown promising results using machine learning to predict surgical outcomes, indicating that this approach has potential for success.
Where this research is happening
New York, United States
- Icahn School of Medicine at Mount Sinai — New York, United States (Active)
Researchers
- Principal investigator: Hofer, Ira — Icahn School of Medicine at Mount Sinai
- Study coordinator: Hofer, Ira
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.