# SCH: Machine learning for personalized preventative intervention in perinatal depression

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2024 · $277,767

## Abstract

PROJECT SUMMARY (See instructions}:
The goal for this project is to develop machine learning (ML) to accelerate the discovery of scalable,
interpretable, and personalized preventative interventions for perinatal depression. Approximately 15% of
pregnant individuals experience perinatal depression, which can have devastating long-term
consequences. Suicide is a leading cause of death among new mothers in the U.S. However,
individualized preventive interventions are not routinely offered at present due to lack of routine screening
practices and limited resources. Machine learning offers an opportunity to improve mental health services
during the perinatal period by identifying patients who would benefit from specific preventative
interventions. We will develop fundamental advances in ML techniques for the discovery of personalized
interventions as well as advances in the social science of incorporating domain and lived experience into
algorithmic systems. Our specific aims bridge prediction with the adaptive experimentation needed to
identify personalized interventions. In Aim 1, we will develop methods which use existing historical data to
lay the groundwork for a randomized experiment of interventions, including to robustly inform which
variables to measure and how to set an initial allocation policy based on those variables. In Aim 2, we will
elicit domain expertise from clinicians and lived experience expertise from perinatal individuals via semistructured
interviews which will inform both the requirements for a trustworthy and implementable ML
system and a structured representation of clinical expertise that can be incorporated to initialize a ML policy
together with historical data. Finally, in Aim 3, we will synthesize these products into an integrated
framework for online learning to discover personalized preventative interventions. The key component of
this framework is continued interaction with patients to provide intermediate feedback and accelerate
convergence to a high-quality policy for allocating preventative interventions.

## Key facts

- **NIH application ID:** 11060670
- **Project number:** 1R01MH139097-01
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** Tamar Krishnamurti
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $277,767
- **Award type:** 1
- **Project period:** 2024-09-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11060670

## Citation

> US National Institutes of Health, RePORTER application 11060670, SCH: Machine learning for personalized preventative intervention in perinatal depression (1R01MH139097-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11060670. Licensed CC0.

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