TY - JOUR
T1 - Development and validation of a machine learning-based postpartum depression prediction model
T2 - A nationwide cohort study
AU - Hochman, Eldar
AU - Feldman, Becca
AU - Weizman, Abraham
AU - Krivoy, Amir
AU - Gur, Shay
AU - Barzilay, Eran
AU - Gabay, Hagit
AU - Levy, Joseph
AU - Levinkron, Ohad
AU - Lawrence, Gabriella
N1 - Publisher Copyright:
© 2020 Wiley Periodicals LLC
PY - 2021/4/1
Y1 - 2021/4/1
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. 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.
AB - 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.
KW - electronic health record data
KW - machine learning
KW - postpartum depression
KW - prediction model
UR - http://www.scopus.com/inward/record.url?scp=85097284328&partnerID=8YFLogxK
U2 - 10.1002/da.23123
DO - 10.1002/da.23123
M3 - Article
C2 - 33615617
AN - SCOPUS:85097284328
SN - 1091-4269
VL - 38
SP - 400
EP - 411
JO - Depression and Anxiety
JF - Depression and Anxiety
IS - 4
ER -