Personalizing treatment by combining multiple health data sources
Statistical Learning for Precision Medicine Based on Multi-Source Data
This project builds smart tools that combine medical records, trial data, and other health information to help tailor treatments to individual patients.
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-11264809 on NIH RePORTER |
What this research studies
From a patient's perspective, researchers are creating computer methods to pull information together from different hospitals, clinical trials, and datasets even when each source records different measurements. They will use transfer-learning techniques to borrow useful patterns from larger datasets to improve predictions for people in smaller or under-represented groups. The team will align differing data features using models informed by medical knowledge graphs, protect privacy across sources, and create ways to choose the best prediction method for a given patient. The goal is to turn these methods into software researchers and clinicians can use to make more personalized treatment decisions.
Who could benefit from this research
Good fit: Patients whose electronic health records or clinical-trial data are available through participating hospitals or datasets, especially those in smaller subgroups with limited existing data, would be most relevant to this work.
Not a fit: Patients without digital records, whose conditions are not represented in the contributing datasets, or who do not allow their data to be used may not see immediate benefit.
Why it matters
Potential benefit: If successful, this work could help clinicians choose more effective and cost-efficient treatments for individual patients by improving how diverse health data are combined.
How similar studies have performed: Related machine-learning and transfer-learning approaches have shown promise in research settings, but integrating many real-world clinical datasets and translating results into routine care remains relatively novel.
Where this research is happening
Stanford, United States
- Stanford University — Stanford, United States (Active)
Researchers
- Principal investigator: Tian, Lu — Stanford University
- Study coordinator: Tian, Lu
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.