# Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomes

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2022 · $437,217

## Abstract

PROJECT SUMMARY
The primary objectives of this project include understanding the interplay between molecular, genetic
and clinical factors related to adverse pregnancy outcomes (APOs), method development for accurate
risk assessment of APOs well before they occur, and method development for collecting additional
clinical data in routine treatment of at-risk-subjects. Towards these goals we have assembled a team of
investigators with clinical, translational, and computational expertise capable of identifying novel
contributors to APOs as well as facilitating clinician-patient interactions using data-driven and
theoretically sound machine learning approaches. Our strategies will rely on advanced machine
learning as well as integration of clinical, genetic, and molecular data and hold promise to bring
precision medicine to the treatment and experience of women during and post pregnancy. We will
predominantly rely on the data collected during the national “Nulliparous Pregnancy Outcomes Study:
monitoring mothers-to-be”; i.e., the nuMoM2b study. Using the cohort of 10,038 nulliparous women, we
will efficiently accomplish 3 Aims: to integrate genetic, clinical, and molecular features towards a deep
understanding of APOs; to develop machine learning models for advanced risk prediction; and to
engage in active data collection towards risk assessment and model development. Using a close
collaboration between computational and clinical scientists, we believe this proposal will result in
important advances in understanding the molecular and clinical aspects of APOs as well as assessing
the risk for APOs and thus providing tangible contributions to maternal health.

## Key facts

- **NIH application ID:** 10453757
- **Project number:** 5R01HD101246-03
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** DAVID M. HAAS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $437,217
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10453757

## Citation

> US National Institutes of Health, RePORTER application 10453757, Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomes (5R01HD101246-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10453757. Licensed CC0.

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