Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas.
Gradient Boosting model accurately predicted postpartum PTSD risk in primiparas
Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas.
To analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (postpartum post-traumatic stress disorder) in primiparas based on the Integrated Framework for Population Health Risk Management (Integrated Framework for Population Health Risk Management), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.
Were divided chronologically into a training cohort and an independent temporal validation cohort.
Among the 794 participants in the training cohort, the incidence of postpartum post-traumatic stress disorder was 25.18%.
Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of postpartum post-traumatic stress disorder, whereas higher social support, greater husband's participation, and parental assistance in neonatal care were associated with a reduced risk.
Postpartum PTSD (postpartum post-traumatic stress disorder) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother-infant relationship, and overall family functioning.