Finding why treatments work differently for different people with smarter data tools
Uncovering Heterogeneity in Individual Treatment Responses via Causal Machine Learning
Using advanced computer methods to find which patients are most likely to benefit from specific medical treatments.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brown University NIH-funded |
| Lab location | 1 site (Providence, United States) |
| Project ID | NIH-11379616 on NIH RePORTER |
What this research studies
If you're a patient, this project aims to use large medical and biological datasets to learn why some people respond to a treatment while others do not. The team will build new causal machine-learning tools that try to uncover hidden predictors of treatment benefit from high‑dimensional data like genetics, biomarkers, and electronic health records. They will test and refine these tools using existing clinical trial and real‑world databases so the results point to reliable patient characteristics. The goal is to produce markers and rules that could help match treatments to the right people.
Who could benefit from this research
Good fit: Ideal candidates are people whose treatment, outcome, and biological or clinical data (for example medical records, genetics, or biomarker tests) are included in clinical trials, registries, or large healthcare databases.
Not a fit: People without their medical or biological data in the datasets used, or those with very rare conditions not represented in the data, are unlikely to see a direct benefit from this grant's work.
Why it matters
Potential benefit: If successful, this work could help doctors personalize treatments so you are more likely to get a therapy that helps you and avoid ones that won't.
How similar studies have performed: Previous machine‑learning and subgroup methods have sometimes identified useful patient groups, but applying causal machine‑learning to uncover unexpected, reliable predictors is a newer and less-tested approach.
Where this research is happening
Providence, United States
- Brown University — Providence, United States (Active)
Researchers
- Principal investigator: Chiu, Yu-Han — Brown University
- Study coordinator: Chiu, Yu-Han
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.