ArticleScientific reports2021
Predicting women with depressive symptoms postpartum with machine learning methods.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Analysis of postpartum and other depression detection using machine learning and deep learning techniques: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled it
- Artificial intelligence in the prevention and early detection of postpartum depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Identifying postpartum depression subtypes using natural language processing and clinical notes.BMJ mental health · 2026Article
- Who's at risk for emergent depression years later? Predictive modeling in a nine-year longitudinal cohort.BMC psychiatry · 2026Article
- AI for Detecting and Predicting Postpartum Depression: Scoping Review.Journal of medical Internet research · 2026Article
- Key predictors of postpartum depression and anxiety symptoms among mothers in Kilifi, Kenya: a machine learning approach.Frontiers in psychiatry · 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
- Predicting allergy and postpartum depression from an incomplete compositional microbiome.BMC genomics · 2025Article
- Identifying prenatal risk factors of postpartum depression with machine learning.Scientific reports · 2025Article
- Building a Machine Learning Model to Predict Postpartum Depression from Electronic Health Records in a Tertiary Care Setting.Journal of clinical medicine · 2025Article
- PERI_DEP: A dataset of mother's mental health in Pakistan.Data in brief · 2025Article
- Association of preoperative anxiety and depression with quality of recovery after caesarean delivery: a prospective observational study.JA clinical reports · 2025Article
- A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study.Frontiers in public health · 2025Article
- Development and validation of an interpretable machine learning model and online web-based calculator based on social-ecosystem theory for early prediction of postpartum depression: a longitudinal study.Frontiers in public health · 2025Article
- Prediction of perinatal depression among women in Pakistan using Hybrid RNN-LSTM model.PeerJ. Computer science · 2025Article
- Evaluating Bias-Mitigated Predictive Models of Perinatal Mood and Anxiety Disorders.JAMA network open · 2024Article
- Predicting first time depression onset in pregnancy: applying machine learning methods to patient-reported data.Archives of women's mental health · 2024Observational
- Predicting Prenatal Depression and Assessing Model Bias Using Machine Learning Models.Biological psychiatry global open science · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness.
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