TY  - JOUR
PY  - 2020//
TI  - Development and validation of a machine learning-based postpartum depression prediction model: a nationwide cohort study
JO  - Depression and anxiety
A1  - Hochman, Eldar
A1  - Feldman, Becca
A1  - Weizman, Abraham
A1  - Krivoy, Amir
A1  - Gur, Shay
A1  - Barzilay, Eran
A1  - Gabay, Hagit
A1  - Levy, Joseph
A1  - Levinkron, Ohad
A1  - Lawrence, Gabriella
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 1091-4269
UR  - http://dx.doi.org/10.1002/da.23123
ID  - ref1
ER  -