Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Baseline radon concentrations in uranium-rich and faulted zones of the Eastern Cordillera, Central Andes, Peru.

Journal of environmental radioactivity·2026

Same author

Trajectories of depressive symptoms across pregnancy and the extended postpartum period and future cardiovascular health.

medRxiv : the preprint server for health sciences·2026

Same author

Single-nucleus profiling reveals a core disease signature and cell type-specific vulnerabilities in early Rett syndrome.

Science advances·2026

Same author

Perinatal mental health screening and care among those with pregnancy complications.

American journal of obstetrics & gynecology MFM·2026

Same author

Prevalence and determinants of fully vaccinated status for COVID-19 among pregnant women in Brazil, Ghana, Kenya, and Pakistan.

Vaccine·2026

Same author

A massively parallel reporter assay of <i>MECP2</i> cis-regulatory elements reveals genetic candidates for male-biased autism.

bioRxiv : the preprint server for biology·2026

Same journal

Comparative Evaluation of Pretrained Large Language Models for Suicide Risk Prediction from Clinical Notes in U.S. Veterans.

medRxiv : the preprint server for health sciences·2026

Same journal

Nocturnal Respiratory Rate and Variability Predict Long-term Mortality in Stable Outpatients with Cardiovascular Disease.

medRxiv : the preprint server for health sciences·2026

Same journal

MOSAIC: Methylation-Oriented Site Analysis and Information Classifier for Robust Epigenomic Classification of Acute Leukemia in Clinical Cohorts with Variable Tumor Purity.

medRxiv : the preprint server for health sciences·2026

Same journal

Risk beliefs, intensive digital information and demand for a new preventative health product in public clinics: Evidence from an experiment in Zimbabwe.

medRxiv : the preprint server for health sciences·2026

Same journal

Development of an automated, imaging-based preoperative screening model for early identification of malnutrition in an abdominal surgery cohort.

medRxiv : the preprint server for health sciences·2026

Same journal

A Pilot Project Leveraging Large Language Models for Automated Screening and Variable Extraction in Observational Studies.

medRxiv : the preprint server for health sciences·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.

Daniel Palacios^1,2,3, Sukru Aras^4,2,3, Yi Zhong^4,2,3

¹Quantitative Computational Biosciences, Baylor College of Medicine, Houston, Texas 77030, USA.

Medrxiv : the Preprint Server for Health Sciences

|December 3, 2025

Summary

This summary is machine-generated.

A new AI system, ClinPreAI, can predict postpartum depression (PPD) risk using electronic health records. This autonomous agent improves early identification, making advanced predictive modeling more accessible for maternal mental health.

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

Related Experiment Videos

Last Updated: Jan 9, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

Area of Science:

Artificial Intelligence
Clinical Informatics
Mental Health

Background:

Postpartum depression (PPD) affects 10-15% of mothers annually, with early identification being a significant clinical challenge.
Existing methods for PPD risk prediction often lack efficiency and accessibility in clinical settings.

Purpose of the Study:

To introduce ClinPreAI, an agentic AI system designed for autonomous development and evaluation of PPD risk prediction models.
To leverage multimodal electronic health record (EHR) data for enhanced PPD prediction accuracy.

Main Methods:

Analysis of EHR data from 4,161 pregnant individuals, including 27 structured clinical variables and social worker notes.
Development and implementation of ClinPreAI, an agentic AI system with five modules for iterative model refinement via autonomous experimentation.
Evaluation of predictive performance using the Edinburgh Postnatal Depression Scale (EPDS) score ≥10 as the primary outcome.

Main Results:

ClinPreAI achieved an F1 score of 0.68 ± 0.03 on structured data, surpassing traditional AutoML and commercial solutions.
On multimodal data, ClinPreAI achieved an F1 score of 0.65 ± 0.04, matching custom LLM-XGBoost and outperforming zero-shot models.
Demonstrated the capability of agentic AI in democratizing sophisticated predictive modeling for clinical applications.

Conclusions:

Agentic AI systems like ClinPreAI can automate the design, implementation, and evaluation of clinical prediction tools.
This approach lowers barriers for developing robust predictive models in healthcare, especially where ML expertise is limited.
ClinPreAI represents a significant advancement in applying autonomous AI for perinatal mental health prediction and clinical decision support.