Using advanced algorithms to analyze complex electronic health records for better patient predictions
Official title: Stochastic Deep Learning for Electronic Health Records: Localizing Learning with Massive and Fragmented Data
Funded at Purdue University · NIH-10933028 · Data updated 2026-11-12
This study is working on a new tool to help doctors make better predictions about your health by analyzing different types of medical data, even when some information is missing, so that you can receive more personalized and effective care.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Purdue University NIH-funded |
| Lab location | 1 site (West Lafayette, United States) |
| Project start | September 25, 2023 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $199,999 |
| NIH project number | 5R01GM152717-02 |
| Project ID | NIH-10933028 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients whose health records are complex and fragmented, particularly those with diverse medical histories.
✕Less likely to help
Patients with straightforward health records or those whose data is complete and consistent may not benefit from this research.
What this research studies
This research focuses on developing a new type of neural network called StoNet to analyze electronic health records (EHR) that are often incomplete and varied among different patient groups. By integrating various types of data, including omics and imaging, the project aims to create a health prediction system that can better handle the complexities of EHR data. The StoNet uses a sophisticated training method based on Markov chain Monte Carlo algorithms to improve the accuracy of predictions. Patients may benefit from more personalized and effective healthcare solutions derived from this advanced data analysis.
Why it matters
Potential benefit: If successful, this research could lead to improved health predictions and personalized treatment plans for patients based on their unique health data.
How similar studies have gone: Other research has shown promise in using advanced algorithms for analyzing health data, but the specific approach of StoNet is relatively novel.
Where this research is happening
-
Purdue University
West Lafayette, United States Active
Researchers
- Principal investigator: Liang, Faming — Purdue University
- Study coordinator: Liang, Faming
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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.