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Depression, Postpartum

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Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.

BMC pregnancy and childbirth 40340559
BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combine...
Adult Cholesterol Depression, Postpartum Female Humans Longitudinal Studies Machine Learning Magnesium Peripartum Period Pregnancy Risk Factors
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Early identification of postpartum depression using machine learning.

Psychiatry and clinical neurosciences 38623000
Adult Depression, Postpartum Early Diagnosis Female Humans Machine Learning
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Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

Journal of the American Medical Informatics Association : JAMIA 38531676
OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for cli...
Decision Support Systems, Clinical Depression, Postpartum Electronic Health Records Female Humans Machine Learning Risk Assessment
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Trajectory on postpartum depression of Chinese women and the risk prediction models: A machine-learning based three-wave follow-up research.

Journal of affective disorders 39154983
BACKGROUND: Our study delves into postpartum depression (PPD) extending observation up to six months postpartum, addressing the gap in long-term follow-ups and uncover critical intervention points.
Adult China Cohort Studies Depression, Postpartum East Asian People Female Follow-Up Studies Humans Machine Learning Pregnancy Prevalence Risk Factors Young Adult
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Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample.

Genes 39766785
BACKGROUND: The genetic determinants of peripartum depression (PPD) are not fully understood. Using a multi-polygenic score approach, we characterized the relationship between genome-wide information and the history of PPD in patients with mood disor...
Adult Bipolar Disorder Depression, Postpartum Depressive Disorder, Major Female Genetic Predisposition to Disease Genome-Wide Association Study Humans Italy Machine Learning Multifactorial Inheritance Peripartum Period Pregnancy
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A method for predicting postpartum depression via an ensemble neural network model.

Frontiers in public health 40297040
INTRODUCTION: Postpartum depression (PPD) has numerous adverse impacts on the families of new mothers and society at large. Early identification and intervention are of great significance. Although there are many existing machine learning classifiers...
Adult Depression, Postpartum Female Humans Machine Learning Neural Networks, Computer
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Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.

JMIR medical informatics 39864955
BACKGROUND: Postpartum depression (PPD) is a prevalent mental health issue with significant impacts on mothers and families. Exploring reliable predictors is crucial for the early and accurate prediction of PPD, which remains challenging.
Adult China Depression, Postpartum Female Humans Machine Learning Pregnancy Retrospective Studies Risk Factors ROC Curve
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A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Journal of proteome research 39772732
Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrome...
Adult Biomarkers Blood Proteins Depression, Postpartum Female Humans Machine Learning Principal Component Analysis Proteomics ROC Curve
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Prediction of postpartum depression in women: development and validation of multiple machine learning models.

Journal of translational medicine 40055720
BACKGROUND: Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several...
Adult Algorithms Depression, Postpartum Female Humans Logistic Models Machine Learning Neural Networks, Computer Pregnancy Reproducibility of Results Risk Assessment Risk Factors Young Adult
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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 40234194
AIM: The adoption of artificial intelligence (AI) tools is gaining traction in maternal mental health (MMH) research. Despite its growing usage, little is known about its prospects and challenges in low- and middle-income countries (LMICs). This stud...
Anxiety Artificial Intelligence Depression Depression, Postpartum Developing Countries Female Humans Pregnancy Pregnancy Complications
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