Ethical, multimodal AI for predicting preeclampsia risk in early pregnancy

Who could benefit from this research

What this research studies

This project builds an ethically-centered, multimodal AI model that analyzes structured health data, ultrasound images, and unstructured physician notes from thousands of pregnancies to identify who is at risk of preeclampsia early on. It includes time-aware embeddings, strategies to handle missing data, and external validation with diverse datasets to ensure the model works across populations. The team collaborates with ethics and human factors experts to keep patient needs and values at the forefront of the development and deployment. By incorporating polygenic risk scores for a subset of individuals, the study aims to enhance predictive performance while maintaining privacy and fairness.

Why it matters

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

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About this research

1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
3. For full project details, budget, and progress reports, visit the official NIH RePORTER page.