Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study

Eldar Hochman, Becca Feldman, Abraham Weizman, Amir Krivoy, Shay Gur, Eran Barzilay, Hagit Gabay, Joseph Levy, Ohad Levinkron, Gabriella Lawrence

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Currently, postpartum depression (PPD) screening is mainly based on self-report symptom-based assessment, with lack of an objective, integrative tool which identifies women at increased risk, before the emergent of PPD. We developed and validated a machine learning-based PPD prediction model utilizing electronic health record (EHR) data, and identified novel PPD predictors. Methods: A nationwide longitudinal cohort that included 214,359 births between January 2008 and December 2015, divided into model training and validation sets, was constructed utilizing Israel largest health maintenance organization's EHR-database. PPD was defined as new diagnosis of a depressive episode or antidepressant prescription within the first year postpartum. A gradient-boosted decision tree algorithm was applied to EHR-derived sociodemographic, clinical, and obstetric features. Results: Among the birth cohort, 1.9% (n = 4104) met the case definition of new-onset PPD. In the validation set, the prediction model achieved an area under the curve (AUC) of 0.712 (95% confidence interval, 0.690–0.733), with a sensitivity of 0.349 and a specificity of 0.905 at the 90th percentile risk threshold, identifying PPDs at a rate more than three times higher than the overall set (positive and negative predictive values were 0.074 and 0.985, respectively). The model's strongest predictors included both well-recognized (e.g., past depression) and less-recognized (differing patterns of blood tests) PPD risk factors. Conclusions: Machine learning-based models incorporating EHR-derived predictors, could augment symptom-based screening practice by identifying the high-risk population at greatest need for preventive intervention, before development of PPD.

Original languageEnglish
Pages (from-to)400-411
Number of pages12
JournalDepression and Anxiety
Volume38
Issue number4
DOIs
StatePublished - Apr 2021

Keywords

  • electronic health record data
  • machine learning
  • postpartum depression
  • prediction model

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