Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Apr 16, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.4K

A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning.

Santiago Jiménez-Serrano1, Salvador Tortajada1,2, Juan Miguel García-Gómez1,2,3

  • 11 Biomedical Informatics Group, Institute for the Applications of Advanced Information and Communication Technologies (ITACA), Polytechnic University of Valencia , Valencia, Spain .

Telemedicine Journal and E-Health : the Official Journal of the American Telemedicine Association
|March 4, 2015
PubMed
Summary

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

Design and first experimental results of an edgeless PET prototype for breast imaging: DeepBreast.

EJNMMI physics·2026
Same author

Quantifying Epistemic Uncertainty in Predictions for Safer Health AI Performance Under Dataset Shifts.

Studies in health technology and informatics·2025
Same author

Design and proof of concept of a double-panel TOF-PET system.

EJNMMI physics·2024
Same author

Unlocking Bevacizumab's Potential: rCBV<sub>max</sub> as a Predictive Biomarker for Enhanced Survival in Glioblastoma IDH-Wildtype Patients.

Cancers·2024
Same author

A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization.

Scientific reports·2023
Same author

Tracking SARS-CoV-2 introductions in Mozambique using pandemic-scale phylogenies: a retrospective observational study.

The Lancet. Global health·2023
This summary is machine-generated.

A new screening tool effectively predicts postpartum depression (PPD) risk within the first week after childbirth. This Naive Bayes model, integrated into an m-health app, offers a sensitive, specific, and cost-effective solution for early PPD detection.

Area of Science:

  • Medical Informatics
  • Machine Learning in Healthcare
  • Maternal Health

Background:

  • Postpartum depression (PPD) is frequently undiagnosed, necessitating effective screening tools.
  • Developing an ideal PPD screening program requires balancing sensitivity, specificity, ease of use, cultural sensitivity, and cost-effectiveness.
  • Early detection of PPD is crucial for timely intervention and improved maternal outcomes.

Purpose of the Study:

  • To develop machine learning models for early detection of postpartum depression risk within the first week postpartum.
  • To create a user-friendly m-health application for PPD screening accessible to mothers and clinicians.
  • To identify the most effective predictive model for PPD screening.

Main Methods:

  • Machine learning techniques were employed to train predictive models using data from postpartum women across seven Spanish hospitals.
Keywords:
Androidclassifiersdecision supportmachine learningmobile healthpattern recognitionpostpartum depression

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

8.3K

Related Experiment Videos

Last Updated: Apr 16, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

2.4K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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

Published on: May 15, 2020

8.3K
  • An internal evaluation using a hold-out strategy assessed model performance.
  • A mobile health application was designed with a clear flowchart and user interface architecture.

    • Main Results:

    • The Naive Bayes model demonstrated the optimal balance between sensitivity and specificity for predicting PPD in the early postpartum period.
    • This high-performing model was successfully integrated into an Android m-health application serving as a clinical decision support system.

    Conclusions:

    • The developed approach facilitates early prediction and detection of postpartum depression.
    • The screening method meets the criteria for an effective test: high sensitivity and specificity, rapid performance, ease of interpretation, cultural sensitivity, and cost-effectiveness.
    • The m-health application provides a practical tool for widespread PPD screening and monitoring.