Identifying patients at high risk for infections from resistant bacteria using advanced technology.
Using natural language processing and machine learning to identify patients at high risk of potentially preventable carbapenem-resistant Enterobacterales (CRE) infection
This study is looking to help doctors find patients who might be carrying a tough-to-treat germ called carbapenem-resistant Enterobacterales (CRE) without showing any symptoms, so they can catch it early and prevent it from spreading in hospitals.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Maryland Baltimore NIH-funded |
| Lab location | 1 site (Baltimore, United States) |
| Project ID | NIH-10906039 on NIH RePORTER |
What this research studies
This research focuses on improving the identification of patients who are at high risk for infections caused by carbapenem-resistant Enterobacterales (CRE), which can lead to severe health complications. By utilizing advanced machine learning and natural language processing techniques, the study aims to analyze electronic health records to detect asymptomatic colonization of CRE in patients. This approach seeks to overcome current diagnostic limitations and enhance early detection, ultimately aiming to reduce the spread of these infections within hospitals. The research will involve data from over 21,000 patients screened for CRE at two major hospitals in Baltimore.
Who could benefit from this research
Good fit: Ideal candidates for this research are hospitalized patients who may be asymptomatically colonized with CRE or at high risk for such colonization.
Not a fit: Patients who are not hospitalized or those who do not have risk factors for CRE colonization may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could significantly reduce the incidence of CRE infections in hospitals, leading to improved patient outcomes and lower mortality rates.
How similar studies have performed: Other research has shown promise in using machine learning and NLP for similar predictive modeling in healthcare, indicating potential success for this approach.
Where this research is happening
Baltimore, United States
- University of Maryland Baltimore — Baltimore, United States (Active)
Researchers
- Principal investigator: Goodman, Katherine Elizabeth — University of Maryland Baltimore
- Study coordinator: Goodman, Katherine Elizabeth
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.