Personalized prediction of diabetes complications from large health records
Transforming Precision Medicine: Dynamic Learning and Prediction of Disease Progression in Massive, Diverse, and Multimodal Cohorts
This project will build tools that use large sets of medical records and biobank data to help predict when people with diabetes might develop complications so care can be tailored earlier.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California Los Angeles NIH-funded |
| Lab location | 1 site (Los Angeles, United States) |
| Project ID | NIH-11299461 on NIH RePORTER |
What this research studies
Researchers will combine electronic health records, biobanks, and other clinical data from very large groups (for example VA records, UK Biobank, and All of Us) to track health changes over time. They will develop new statistical methods, computational algorithms, and user-friendly software to turn complex, multimodal data into individual risk timelines. The work focuses on complications of diabetes and on identifying time-varying risk factors and early warning signs. If successful, these tools could help clinicians adjust monitoring or treatment for individual patients based on changing risk.
Who could benefit from this research
Good fit: Ideal candidates are adults with diabetes whose longitudinal medical records or biobank samples are available in large healthcare datasets or who can consent to share their clinical data for research.
Not a fit: People without long-term electronic health records, those not represented in the source cohorts, or those seeking immediate therapeutic interventions are unlikely to gain direct benefit from this methods-focused project.
Why it matters
Potential benefit: Could provide earlier, personalized warnings about worsening diabetes and guide clinicians to prevent or delay complications.
How similar studies have performed: Some prior prediction models for diabetes complications have shown promise, but scaling and generalizing these approaches across much larger, more diverse, and multimodal datasets remains only partially tested.
Where this research is happening
Los Angeles, United States
- University of California Los Angeles — Los Angeles, United States (Active)
Researchers
- Principal investigator: Zhou, Jin — University of California Los Angeles
- Study coordinator: Zhou, Jin
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.