# Predictive modeling of peripartum depression

> **NIH NIH R03** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $84,000

## Abstract

ABSTRACT
Psychiatric problems surrounding parturition affect both the mother’s health and her child’s developmental
trajectory. Peripartum depression (PPD), referring to a depressive episode occurring during pregnancy or after
childbirth, is both common and morbid. PPD has been implicated in various short and long term adverse
outcomes, including preterm delivery and heightened risk for mental illness in the adult offspring. In extreme
cases, PPD can lead to maternal suicide and/or infanticide. Although an estimated 760,000 American women
(and children) suffer from PPD each year and screening for PPD has been recommended by the USPTF, no
accurate screening tool is available to adequately identify women at risk of PPD.
This novel study will capitalize on the rich clinical, demographic, and laboratory information in patients’ electronic
medical reports (EMRs) to improve screening for PPD. We propose to implement advanced machine learning
methods to build a model to optimize identification of women at risk for PPD. We we will adopt a psycho-social-
biological approach of mental illness to prospectively explore the combined effect of various disease-related
factors in improving the accuracy of PPD prediction. Our dataset will make use of a sample of 20,000 women
who have been followed during their obstetrical care in two leading academic hospitals in Boston. We will gather
information concerning socioeconomic factors, relevant obstetric factors, and mental and physical conditions in
pregnancy and disease history, as derived from laboratory test results and the patient’s report.
We expect our findings to advance scientific knowledge of women at risk for PPD. Our work may lead to the
development of a screening protocol that is low-cost and easily performed by health providers in clinical settings.
Early identification of women at risk could potentially allow targeted interventions to reduce the prevalence and
morbidity of PPD in the US. This in turn could reduce treatment costs, avoid a potentially preventable disease,
and improve the quality of care and health outcomes of mothers and their children. Our study accords with the
NICD high priority area of research aimed at improving the health of women during and after pregnancy and
improving pregnancy outcomes. The proposed project will further the NICHD mission that women suffer no
harmful effects from reproductive processes, and that children achieve healthy and productive lives.

## Key facts

- **NIH application ID:** 10131234
- **Project number:** 5R03HD101724-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Sharon Dekel
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $84,000
- **Award type:** 5
- **Project period:** 2020-04-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10131234

## Citation

> US National Institutes of Health, RePORTER application 10131234, Predictive modeling of peripartum depression (5R03HD101724-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10131234. Licensed CC0.

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