Predicting women with depressive symptoms postpartum with machine learning methods

Sam Andersson,Deepti R Bathula,Stavros I Iliadis,Alkistis Skalkidou,Martin Walter

doi:10.1038/s41598-021-86368-y

Abstract

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers’ and children’s health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness.

Highlights

ObjectivesWe aim to predict women at risk for depressive symptoms at 6 weeks postpartum, from clinical, demographic, and psychometric questionnaire data available after childbirth, by use of machine learning methods
Background & Survey DataData Management Data Preprocessing Missing Values ImputaƟon Feature ExtracƟon Scaling & RepresentaƟonK-Fold Cross ValidaƟon TrainingModel Feature Parameter Model Training SelecƟon Tuning EvaluaƟon Test Final Model PredicƟons
Differences were seen among women with depressive symptoms and women without depressive symptoms across sociodemographic variables like education, employment, and country of origin, as well as many other variables known as risk factors for postpartum depression

Summary

Objectives

We aim to predict women at risk for depressive symptoms at 6 weeks postpartum, from clinical, demographic, and psychometric questionnaire data available after childbirth, by use of machine learning methods

Methods

Results

Conclusion