ArticleBMC pregnancy and childbirth2021
Estimation of postpartum depression risk from electronic health records using machine learning.
Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
34 citing papers in PubMed, 4 syntheses or guidelines pooled it, 74 citations in OpenAlex.
- Analysis of postpartum and other depression detection using machine learning and deep learning techniques: a systematic review.Frontiers in artificial intelligence · 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-oriented predictive model for the risk of postpartum depression: a systematic review.Frontiers in public health · 2025Pooled it
- Pooled it
- Implementation of a clinical decision support tool for postpartum depression: protocol for a prospective randomised clinical trial.BMJ open · 2026Article
- ClinPreAI: An Agentic AI System for Early Postpartum Depression Risk Prediction from Multimodal EHR Data.medRxiv : the preprint server for health sciences · 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
- Bridging the awareness-action gap in pelvic floor prehabilitation: cognitive barriers and facilitators for asymptomatic populations-a perspective.Frontiers in public health · 2026Article
- 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
- Machine learning-based nomogram for predicting depressive symptoms in women: A cross-sectional study in Guangdong Province, China.World journal of psychiatry · 2025Article
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study.JMIR formative research · 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
- Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.BMC medical informatics and decision making · 2025Article
- A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.Journal of proteome research · 2025Article
- Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.JMIR medical informatics · 2025Article
- The use of artificial intelligence in sexual and reproductive health: a comprehensive scoping review.npj women's health · 2025Article
- Navigating promise and perils: applying artificial intelligence to the perinatal mental health care cascade.npj health systems · 2025Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundPostpartum depression is a widespread disorder, adversely affecting the well-being of mothers and their newborns. We aim to utilize machine learning for predicting risk of postpartum depression (PPD) using primary care electronic health records (EHR) data, and to evaluate the potential value of EHR-based prediction in improving the accuracy of PPD screening and in early identification of women at risk.
methodsWe analyzed EHR data of 266,544 women from the UK who gave first live birth between 2000 and 2017. We extracted a multitude of socio-demographic and medical variables and constructed a machine learning model that predicts the risk of PPD during the year following childbirth. We evaluated the model's performance using multiple validation methodologies and measured its accuracy as a stand-alone tool and as an adjunct to the standard questionnaire-based screening by Edinburgh postnatal depression scale (EPDS).
resultsThe prevalence of PPD in the analyzed cohort was 13.4%. Combing EHR-based prediction with EPDS score increased the area under the receiver operator characteristics curve (AUC) from 0.805 to 0.844 and the sensitivity from 0.72 to 0.76, at specificity of 0.80. The AUC of the EHR-based prediction model alone varied from 0.72 to 0.74 and decreased by only 0.01-0.02 when applied as early as before the beginning of pregnancy.
conclusionsPPD risk prediction using EHR data may provide a complementary quantitative and objective tool for PPD screening, allowing earlier (pre-pregnancy) and more accurate identification of women at risk, timely interventions and potentially improved outcomes for the mother and child.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.