Predicting postpartum bleeding risks using advanced algorithms and genetic information
Official title: Personalized Postpartum Hemorrhage Prediction Using Machine Learning And Polygenic Risk Scores
Funded at Brigham and Women's Hospital · NIH-10888319 · Data updated 2026-11-12
This study is working on a new tool to help predict the risk of heavy bleeding after childbirth, using advanced technology to look at both medical history and genetic information, so that new moms can get better care and support during and after delivery.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Brigham and Women's Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | August 1, 2022 |
| Project end | July 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $168,480 |
| NIH project number | 5K08HL161326-03 |
| Project ID | NIH-10888319 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include pregnant women, particularly those with a family history of postpartum hemorrhage or other risk factors.
✕Less likely to help
Patients who are not pregnant or those who have already experienced childbirth without complications may not benefit from this research.
What this research studies
This research focuses on developing a personalized tool to predict the risk of postpartum hemorrhage, which is excessive bleeding after childbirth. By utilizing machine learning techniques and polygenic risk scores, the study aims to create a more accurate prediction model than existing tools, which often miss a significant number of cases. The approach involves analyzing various clinical and genetic factors to improve decision-making and reduce biases in predicting hemorrhage risks. This could lead to better management and outcomes for women during and after childbirth.
Why it matters
Potential benefit: If successful, this research could significantly reduce the incidence of postpartum hemorrhage and improve maternal health outcomes.
How similar studies have gone: Other research has shown promise in using machine learning for predictive modeling in healthcare, indicating that this approach could be effective.
Where this research is happening
-
Brigham and Women's Hospital
Boston, United States Active
Researchers
- Principal investigator: Kovacheva, Vesela — Brigham and Women's Hospital
- Study coordinator: Kovacheva, Vesela
Related research
Other NIH-funded projects on the same topics.
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Wearable tech to track blood loss during childbirth
A wearable device monitors cardiovascular signals to detect postpartum hemorrhage early so clinicians can act quickly to protect mothers.
-
Maternal Postpartum Monitoring with a Wearable Sensor (MOMS)
A wearable system called MOMS will continuously monitor multiple physiological signals in pregnant people to detect postpartum hemorrhage earlier than standard vital signs.
-
AI-powered real-time postpartum hemorrhage risk predictor in clinical care
A real-time AI tool integrated into electronic health records helps identify women at risk for postpartum hemorrhage so clinicians can intervene earlier.
-
Targeting Ubiquitination to Improve Uterotonic Therapies in Postpartum Hemorrhage
A new approach aims to boost uterine contractions during childbirth and after delivery by understanding how a hormone receptor is regulated, potentially reducing postpartum hemorrhage risk.
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Personalized Postpartum Bleeding Risk Prediction with Machine Learning and Genetic Scores
This grant aims to create a personalized tool that predicts a mother’s risk of postpartum hemorrhage using machine learning and her genetic information.
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OPTIMUM OB-TXA: Timing, route, and dose of tranexamic acid to prevent postpartum bleeding before cord clamping
Aims to find the best time, method, and amount of tranexamic acid given before cord clamping to prevent postpartum hemorrhage in new mothers and protect babies.
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.