Studies in health technology and informatics
Aug 7, 2025
Postpartum depression (PPD) affects approximately 20% of pregnant individuals, yet half of these cases remain under-treated despite the availability of educational interventions. To address this gap, the Supporting Personalized prEgnancy Care wIth Ar...
Studies in health technology and informatics
Aug 7, 2025
Postpartum depression (PPD) affects approximately 20% of women after childbirth and has complex etiology. Existing predictive models of PPD lack training on large, national datasets and comprehensive integration of clinical and social determinants. T...
OBJECTIVE: Postpartum depression (PPD) is a major contributor to postpartum morbidity and mortality. Beyond efforts at routine screening, risk stratification models could enable more targeted interventions in settings with limited resources. The auth...
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...
Journal of the American Medical Informatics Association : JAMIA
May 20, 2024
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...
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