Using machine learning to predict health outcomes in preterm infants.
Official title: Machine Learning and Multiomics for Predictive Models and Biomarker Discovery in Preterm Infants.
Funded at Baylor College of Medicine · NIH-10917279 · Data updated 2026-11-12
This study is looking at tiny babies born early and weighing very little, to find ways to predict their health and survival by examining their unique biological markers and using smart computer techniques, so doctors can provide better, personalized care for them.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Baylor College of Medicine NIH-funded |
| Lab location | 1 site (Houston, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $656,072 |
| NIH project number | 5R01HD112886-02 |
| Project ID | NIH-10917279 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are preterm infants born at less than 32 weeks gestation and weighing less than 1500 grams.
✕Less likely to help
Patients who are not preterm or do not fall within the very low birth weight category may not benefit from this research.
What this research studies
This research focuses on preterm infants born with very low birth weight, aiming to develop predictive models that integrate clinical data and multi-omic signatures. By analyzing microbial metabolites and their impact on health, the study seeks to identify biomarkers that can predict survival and major health issues in these vulnerable infants. The approach involves leveraging advanced machine learning techniques to create accurate models that can inform targeted interventions in neonatal care. Ultimately, the goal is to shift from traditional to precision medicine in treating preterm infants.
Why it matters
Potential benefit: If successful, this research could significantly improve survival rates and health outcomes for preterm infants.
How similar studies have gone: Previous research has shown promise in using machine learning and multi-omics approaches for predictive modeling in various medical fields, suggesting potential success in this novel application.
Where this research is happening
-
Baylor College of Medicine
Houston, United States Active
Researchers
- Principal investigator: Pammi, Mohan — Baylor College of Medicine
- Study coordinator: Pammi, Mohan
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PREEMIE PROGRESS: a family program to help parents of very preterm babies
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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.