@article{ref1,
title="Development and validation of a machine learning-based postpartum depression prediction model: a nationwide cohort study",
journal="Depression and anxiety",
year="2020",
author="Hochman, Eldar and Feldman, Becca and Weizman, Abraham and Krivoy, Amir and Gur, Shay and Barzilay, Eran and Gabay, Hagit and Levy, Joseph and Levinkron, Ohad and Lawrence, Gabriella",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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. <br><br>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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1091-4269",
doi="10.1002/da.23123",
url="http://dx.doi.org/10.1002/da.23123"
}