Predicting women with depressive symptoms postpartum with machine learning methods

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.

Figure 1

Evaluation of model performance in the dataset containing only background, medical and pregnancy-related variables (n = 4277 women). The models tested were Ridge Regression, LASSO Regression, Distributed Random Forest, Extremely Randomized Trees, Gradient Boosted Machines, Stacked Ensemble, and Naïve Bayes. Models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC), the outcome being depressive symptoms at 6 weeks postpartum. The bars represent the level of performance measures (in percent) and the table below the bar plot presents the exact numerical values. Error bars represent one standard deviation from the mean.

Figure 2

Evaluation of model performance in the total combined dataset (n = 2385 women). The combined dataset contained the background, medical and pregnancy-related variables, as well as answers to the questionnaires Resilience-14, Sense of Coherence-29 and Vulnerable Personality Scale Questionnaire. The models tested were Ridge Regression, LASSO Regression, Distributed Random Forest, Extremely Randomized Trees, Gradient Boosted Machines, Stacked Ensemble, and Naïve Bayes. Models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC), the outcome being depressive symptoms at 6 weeks postpartum. The bars represent the level of performance measures (in percent) and the table below the bar plot presents the exact numerical values. Error bars represent one standard deviation from the mean.

Figure 3

Comparative performance of the dataset containing only background, medical history and pregnancy-related variables (BP) and the combined dataset (BP + RS + SOC + VPSQ). The Extremely Randomized Trees (XRT) algorithm was used to compare the performance of the two datasets for predicting depression at 6 weeks postpartum. Models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). The variable selection procedure shows results when All (100%), Top 50%, and Top 25% of variables were retained, ranked according to Mean Decrease in Impurity (MDI) relevance.

Figure 4

Stratified classification graphs for Extreme Randomized Forest (XRT) model, by pregnancy/previous depression status. Results presented for all women (All, n = 2385, of which 14% had postpartum depression, PPD), women with depression during current pregnancy or earlier in life (With Previous Depression, n = 971, of which 27% had PPD), and women without any previous depression episode (Without Previous Depression, n = 1414, of which 6% had PPD). For each category, models were assessed for accuracy (ACC), sensitivity (SENS), specificity (SPEC), positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC).

Figure 5

Ranked importance of the assessed variables using the Extremely Randomized Trees (XRT) models in the combined dataset, considering the women with different previous depression status. Results presented for all women (A), All women (n = 2385), (B) women with depression during current pregnancy or earlier in life (Previous/pregnancy depression, n = 971), and (C) women without any previous depression episode (No previous depression, n = 1414). The graphs depict the variable importance as a relative measure that is scaled to a maximum of 1.0. The x-axis represents the relative contribution to the classification algorithm of the corresponding feature on the y-axis.

Figure 6

Ranked importance of the assessed background, medical history and pregnancy variables for all women (n = 4277) using Extremely Randomized Trees (XRT) models. The top 25% of the variables are reported. The x-axis represents the relative contribution of the corresponding variable to the classification algorithm.

Figure 7

Study workflow and analytical strategy. Data were obtained from the “Biology, Affect, Stress, Imaging and Cognition during Pregnancy and the Puerperium” (BASIC) study, a population-based prospective cohort study in Uppsala, Sweden. Data included in our study comprised (i) background, medical history and pregnancy-related variables (BP) from women, and (ii) further psychometric questionnaires, available at discharge from the delivery ward. The data were processed and either were used to test models or train the machine learning algorithms, to predict depressive symptoms at 6 weeks postpartum.