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Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study.
- Source :
-
Depression and anxiety [Depress Anxiety] 2021 Apr; Vol. 38 (4), pp. 400-411. Date of Electronic Publication: 2020 Dec 07. - Publication Year :
- 2021
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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 />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 />Results: Among the birth cohort, 1.9% (nā=ā4104) 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 />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.<br /> (© 2020 Wiley Periodicals LLC.)
Details
- Language :
- English
- ISSN :
- 1520-6394
- Volume :
- 38
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Depression and anxiety
- Publication Type :
- Academic Journal
- Accession number :
- 33615617
- Full Text :
- https://doi.org/10.1002/da.23123