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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...

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Updated: Mar 22, 2026

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

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Machine learning-based methods for predicting postpartum depression: A review.

Xiaobo Zhang¹, Jinyu Bao², Jianzhong Ye²

¹School of Computing and Artificial Intelligence, SouthWest JiaoTong University, Chengdu 611756, China; Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education, China.

Artificial Intelligence in Medicine

|March 20, 2026

Summary

This summary is machine-generated.

Machine learning (ML) shows promise for predicting postpartum depression (PPD). Gradient boosting models achieved the best results, indicating ML

Keywords:

Influence factor Machine learning Postpartum depression Prediction models

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Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents

Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents

Published on: June 10, 2013

Area of Science:

Computational psychiatry
Artificial intelligence in healthcare
Reproductive mental health

Background:

Postpartum depression (PPD) significantly impacts maternal and infant well-being.
Machine learning (ML) offers advanced capabilities for disease prediction.

Purpose of the Study:

To review and summarize ML techniques for predicting PPD risk.
To investigate the potential of ML in early PPD identification.

Main Methods:

Bibliographic search across multiple databases (CNKI, CQVIP, Web of Science, Google Scholar).
Inclusion criteria applied to 103 retrieved articles, selecting 25 relevant studies.
Analysis focused on ML models and their predictive performance for PPD.

Main Results:

Supervised learning was the predominant ML approach.
Gradient boosting, random forest, and support vector machines were the most common models.
Gradient boosting models demonstrated superior performance, with most studies achieving an Area Under the Curve (AUC) > 0.7.

Conclusions:

ML techniques are feasible and effective for PPD prediction.
Further research is needed to standardize data, enhance feature selection, and foster collaboration for improved model accuracy and personalized care.