Development and validation of a machine learning-based postpartum depression prediction model: A nationwide cohort study.
Depress Anxiety
; 38(4): 400-411, 2021 04.
Article
em En
| MEDLINE
| ID: mdl-33615617
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.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Depressão Pós-Parto
Tipo de estudo:
Diagnostic_studies
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Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Female
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Humans
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Pregnancy
País/Região como assunto:
Asia
Idioma:
En
Revista:
Depress Anxiety
Assunto da revista:
PSIQUIATRIA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Israel