Clinical Decision Support System for Post-Pregnancy Mental Health

Sep. 2024 – May. 2025

Data-driven modeling for postpartum depression risk prediction and clinical decision support

Overview

This project focuses on developing a clinical decision support system for post-pregnancy loss (PPL) mental health, with an emphasis on early risk prediction for postpartum depression (PPD). The goal is to bridge the gap between data-driven prediction and actionable clinical insights.

The work is conducted at Cornell University under the supervision of Dr. Clifford Whitcomb and Dr. Yiye Zhang.

Problem Motivation

Postpartum depression affects a significant portion of patients, yet early detection remains challenging in clinical practice. Existing approaches often lack integration of social support indicators and real-time predictive capability.

This project investigates how machine learning models can leverage large-scale healthcare data to provide early warnings and support personalized intervention strategies.

Methodology

  • Analyzed PRAMS Phase 8 dataset (2016–2021), containing over 200,000 observations
  • Engineered features including:
    • social support indicators
    • socioeconomic status
    • mental health history
  • Implemented multiple predictive models:
    • regression models
    • tree-based models (Random Forest)
    • Bayesian methods
    • deep neural networks

System Perspective

Beyond prediction, this project incorporates a systems engineering perspective by integrating model outputs into a decision-support framework for clinicians. The objective is to enable actionable, interpretable, and personalized recommendations in healthcare settings.

Poster

See our results in this Poster.