Using machine learning to predict help-seeking among 2016–2018 Pregnancy Risk Assessment Monitoring System participants with postpartum depression symptoms
| Autor: | Rebecca Fischbein, Heather L Cook, Kristin Baughman, Sebastián R Díaz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Women's Health, Vol 18 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1745-5065 17455057 |
| DOI: | 10.1177/17455057221139664 |
| Popis: | Background: Despite the importance of early identification and treatment, postpartum depression often remains largely undiagnosed with unreported symptoms. While research has identified several factors as prompting help-seeking for postpartum depression symptoms, no research has examined help-seeking for postpartum depression using data from a multi-state/jurisdictional survey analyzed with machine learning techniques. Objectives: This study examines help-seeking among people with postpartum depression symptoms using and demonstrating the utility of machine learning techniques. Methods: Data from the 2016–2018 Pregnancy Risk Assessment Monitoring System, a cross-sectional survey matched with birth certificate data, were used. Six US states/jurisdictions included the outcome help-seeking for postpartum depression symptoms and were used in the analysis. An ensemble method, “Super Learner,” was used to identify the best combination of algorithms and most important variables that predict help-seeking among 1920 recently pregnant people who screen positive for postpartum depression symptoms. Results: The Super Learner predicted well and had an area under the receiver operating curve of 87.95%. It outperformed the highest weighted algorithms which were conditional random forest and stochastic gradient boosting. The following variables were consistently among the top 10 most important variables across the algorithms for predicting increased help-seeking: participants who reported having been diagnosed with postpartum depression, having depression during pregnancy, living in particular US states, being a White compared to Black or Asian American individual, and having a higher maternal body mass index at the time of the survey. Conclusion: These results show the utility of using ensemble machine learning techniques to examine complex topics like help-seeking. Healthcare providers should consider the factors identified in this study when screening and conducting outreach and follow-up for postpartum depression symptoms. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|