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

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

You might also read

Related Articles

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

Sort by

Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026

Same journal

Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.

JMIR medical informatics·2026

Same journal

Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.

JMIR medical informatics·2026

Same journal

Assessing the Feasibility and Acceptability of Implementing a Preclinic Vital Signs Assessment in Primary Care: Cross-Sectional Pilot Study.

JMIR medical informatics·2026

Same journal

Candidate Passive Sensor Suite Technologies for Tactical Combat Casualty Care Environments: Comparative Assessment Study.

JMIR medical informatics·2026

Same journal

Relevance of the uMap Collaborative Platform as Support for Choropleth Mapping: A Traffic‒Light Statistical Signal Atlas of All-Cause Mortality-First French Lockdown.

JMIR medical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 30, 2025

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

Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.

Ren Zhang^1,2, Yi Liu³, Zhiwei Zhang²

¹Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.

JMIR Medical Informatics

|January 26, 2025

Summary

This summary is machine-generated.

Machine learning accurately predicts postpartum depression (PPD) using factors like antepartum depression and thyroid levels. This aids early screening for mothers at high risk of PPD.

Keywords:

PPD XGBoost extreme gradient boosting machine learning postpartum depression predictive model risk factors

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

Published on: June 13, 2021

Related Experiment Videos

Last Updated: May 30, 2025

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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

Published on: June 13, 2021

Area of Science:

Reproductive Medicine
Psychiatry
Computational Biology

Background:

Postpartum depression (PPD) significantly impacts maternal and family well-being.
Accurate and early prediction of PPD remains a clinical challenge.

Purpose of the Study:

To develop and validate machine learning models for precise PPD prediction.
To identify key predictors and their clinical implications for PPD.

Main Methods:

Collected data from 2055 pregnant women.
Utilized least absolute shrinkage and selection operator (LASSO) regression for variable screening.
Developed and validated machine learning models using training and validation cohorts.

Main Results:

The extreme gradient boosting model achieved an AUC of 0.849.
Key predictors identified include antepartum depression, lower fetal weight, elevated TSH, decreased TPOAb, elevated ferritin, and older maternal age.
Shapley Additive Explanation (SHAP) was used for model interpretation.

Conclusions:

A validated machine learning model for PPD prediction was developed.
Identified physiological and psychological factors offer insights for early PPD risk screening.
Highlights the need for comprehensive screening approaches for PPD.