<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>18</volume><submitter>Fischbein R</submitter><pubmed_abstract>&lt;h4>Background:&lt;/h4> Despite the importance of early identification and treatment, postpartum</pubmed_abstract><journal>Women's health (London, England)</journal><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9685210</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Using machine learning to predict help-seeking among 2016–2018 Pregnancy Risk Assessment Monitoring System participants with postpartum depression symptoms</pubmed_title><pmcid>PMC9685210</pmcid><pubmed_authors>Baughman K</pubmed_authors><pubmed_authors>Fischbein R</pubmed_authors><pubmed_authors>Cook H</pubmed_authors><pubmed_authors>Diaz S</pubmed_authors></additional><is_claimable>false</is_claimable><name>Using machine learning to predict help-seeking among 2016–2018 Pregnancy Risk Assessment Monitoring System participants with postpartum depression symptoms</name><description>&lt;h4>Background:&lt;/h4> Despite the importance of early identification and treatment, postpartum</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Jan</publication><modification>2025-04-18T22:30:13.411Z</modification><creation>2025-04-07T10:14:48.862Z</creation></dates><accession>S-EPMC9685210</accession><cross_references/></HashMap>