Article

Machine learning-optimized perinatal depression screening: Maximum impact, minimal burden

2025-10-17

Abstract excerpt

<h4>Introduction</h4> Perinatal depression affects up to 30% of pregnant and postpartum women, which has increased since the COVID-19 pandemic, making rapidly identifying affected women a high clinical priority. While screening tools like the Edinburgh Postnatal Depression Scale (EPDS) are widely used, brevity is important for busy clinical practice to reduce administration time and patient burden. Current method...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 0a7c2c74-a64d-5597-a03e-24e669f61a0d
DOI: 10.1101/2025.10.13.25337771

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Machine learning-optimized perinatal depression screening: Maximum impact, minimal burdenDOI 10.1101/2025.10.13.25337771Read

Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancySemantic proximity Read

Stratifying Risk for Postpartum Depression at Time of Hospital DischargeSemantic proximity Read

Life-course comorbidity patterns and integrated prediction of postpartum depression, multimorbidity, and symptom progressionSemantic proximity Read

Key predictors of maternal mild depression and anxiety in low resource settings: A machine learning approachSemantic proximity Read

Exploring Heterogeneity in perinatal depression: a comprehensive review.Semantic proximity Read

Development and Validation of a Novel Culturally-Tailored Postpartum Depression Risk Scoring Scale (PDRS) in Malaysia: A Sequential Mixed-Methods StudySemantic proximity Read

Predicting depression using electronic health records data: A systematic reviewSemantic proximity Read

Digital phenotyping of depression during pregnancy using self-report data.Semantic proximity Read

Predictors of Postpartum Depression: A Comprehensive Review of the Last Decade of Evidence.Semantic proximity Read

Early identification of postpartum depression using demographic, clinical, and digital phenotypingSemantic proximity Read

Development and Validation of Machine Learning-Based Prediction of Depression Progression Using EHR Data: A Multi-Institutional Retrospective Cohort StudySemantic proximity Read

Development and validation of an interpretable longitudinal preeclampsia risk prediction using machine learningSemantic proximity Read