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Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study.

JMIR Med Inform

January 2025

Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.

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.

Objective: This study aimed to comprehensively collect variables from multiple aspects, develop and validate machine learning models to achieve precise prediction of PPD, and interpret the model to reveal clinical implications.

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Objective: To determine whether chronodisruption is associated with achieving pregnancy.

Design: Pilot prospective cohort study.

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Introduction And Importance: The estimated incidence of congenital dislocation of the knee, also referred to as genu recurvatum, is approximately 1 in 100,000 live births. The purpose of this report is to present a rare case of unilateral congenital knee dislocation, highlighting the clinical presentation and management.

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[Correlation between uterine volume and intrauterine adhesion: a propensity score matching analysis].

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