Abstract:
Postpartum depression (PPD) is a detrimental health condition that afects 12% of new mothers.
Despite negative efects on mothers’ and children’s health, many women do not receive adequate
care. Preventive interventions are cost-efcient 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 specifcity (accuracy 73%, sensitivity 72%, specifcity 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-efectiveness.
