Better Antibiotic Choices to Fight Resistant Bacteria
Measuring and Predicting Appropriate Antibiotic Use to Combat Resistant Bacteria
This project aims to create smart tools that help doctors choose the best antibiotics for patients, especially for common infections like UTIs, to fight against bacteria that are becoming harder to treat.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Stanford University NIH-funded |
| Lab location | 1 site (Stanford, United States) |
| Project ID | NIH-11122309 on NIH RePORTER |
What this research studies
We are working to develop smart computer tools that can help doctors pick the right antibiotic for your infection. These tools will use advanced machine learning to predict which antibiotics will work best for you, based on information from many past patient cases. The goal is to make sure you get the most effective treatment quickly, especially for common issues like urinary tract infections, where antibiotic resistance is a growing concern. By improving how antibiotics are prescribed, we hope to slow down the problem of bacteria becoming resistant to medicines.
Who could benefit from this research
Good fit: This research is focused on improving care for patients who experience bacterial infections, particularly common ones like urinary tract infections.
Not a fit: Patients without bacterial infections or those not requiring antibiotic treatment would not directly benefit from this specific research.
Why it matters
Potential benefit: If successful, this work could lead to more effective and personalized antibiotic treatments, reducing the risk of resistant infections and improving patient recovery.
How similar studies have performed: While machine learning is increasingly used in medicine, developing personalized antibiograms for real-time clinical decision support is a novel application with significant potential.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Chen, Jonathan H. — Stanford University
- Study coordinator: Chen, Jonathan H.
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.