ArticleJMIR medical informatics2020
Machine Learning Models for the Prediction of Postpartum Depression: Application and Comparison Based on a Cohort Study.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Role of psychological resilience and social support in perinatal depression: a systematic review and meta-analysis.BMC pregnancy and childbirth · 2026Pooled it
- Leveraging artificial intelligence in the prediction, diagnosis and treatment of depression and anxiety among perinatal women in low- and middle-income countries: a systematic review.BMJ mental health · 2025Pooled it
- Artificial intelligence in the prevention and early detection of postpartum depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- AI for Detecting and Predicting Postpartum Depression: Scoping Review.Journal of medical Internet research · 2026Article
- Development and external validation of an explainable XGBoost model with a web-based calculator for risk prediction of postpartum depressive symptoms.Frontiers in psychiatry · 2026Article
- Building a Machine Learning Model to Predict Postpartum Depression from Electronic Health Records in a Tertiary Care Setting.Journal of clinical medicine · 2025Article
- Machine learning-based risk factor analysis and prediction model construction for mortality in chronic heart failure.Journal of global health · 2025Article
- Maximum and minimum activity in inpatient adolescents with Bipolar Disorders: Daily-Variability classification of actigraphy pattern with artificial intelligence.Psychiatry research communications · 2025Article
- Predictive Analysis of Postpartum Depression Using Machine Learning.Healthcare (Basel, Switzerland) · 2025Article
- Prediction of postpartum depression in women: development and validation of multiple machine learning models.Journal of translational medicine · 2025Article
- Utilization of tree-based machine learning models for predicting low birth weight cases.BMC pregnancy and childbirth · 2025Article
- Identifying key indicators to develop a novel mobile application for early screening of postpartum depression in developing countries.BMC health services research · 2025Article
- Machine Learning-Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study.JMIR medical informatics · 2025Article
- Postpartum depression risk prediction using explainable machine learning algorithms.Frontiers in medicine · 2025Article
- Evaluating Bias-Mitigated Predictive Models of Perinatal Mood and Anxiety Disorders.JAMA network open · 2024Article
- Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models.Biological psychiatry global open science · 2024Article
- Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.JMIR mental health · 2024Article
- Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer.Scientific reports · 2024Article
- Comparative analysis of machine learning versus traditional method for early detection of parental depression symptoms in the NICU.Frontiers in public health · 2024Article
- Predicting Patients Requiring Treatment for Depression in the Postpartum Period Using Common Electronic Medical Record Data Available Antepartum.AJPM focus · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPostpartum depression (PPD) is a serious public health problem. Building a predictive model for PPD using data during pregnancy can facilitate earlier identification and intervention.
objectiveThe aims of this study are to compare the effects of four different machine learning models using data during pregnancy to predict PPD and explore which factors in the model are the most important for PPD prediction.
methodsInformation on the pregnancy period from a cohort of 508 women, including demographics, social environmental factors, and mental health, was used as predictors in the models. The Edinburgh Postnatal Depression Scale score within 42 days after delivery was used as the outcome indicator. Using two feature selection methods (expert consultation and random forest-based filter feature selection [FFS-RF]) and two algorithms (support vector machine [SVM] and random forest [RF]), we developed four different machine learning PPD prediction models and compared their prediction effects.
resultsThere was no significant difference in the effectiveness of the two feature selection methods in terms of model prediction performance, but 10 fewer factors were selected with the FFS-RF than with the expert consultation method. The model based on SVM and FFS-RF had the best prediction effects (sensitivity=0.69, area under the curve=0.78). In the feature importance ranking output by the RF algorithm, psychological elasticity, depression during the third trimester, and income level were the most important predictors.
conclusionsIn contrast to the expert consultation method, FFS-RF was important in dimension reduction. When the sample size is small, the SVM algorithm is suitable for predicting PPD. In the prevention of PPD, more attention should be paid to the psychological resilience of mothers.
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