Machine learning-optimized perinatal depression screening: Maximum impact, minimal burden.
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| Title: | Machine learning-optimized perinatal depression screening: Maximum impact, minimal burden. |
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| Authors: | Hurwitz E; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States., Shell C; Department of OBGYN, Sinai Hospital of Baltimore, Baltimore, MD, United States., Chugh K; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States., Bergink V; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States., Patel RC; Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States., Schiller C; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States., Haendel MA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States. |
| Source: | MedRxiv : the preprint server for health sciences [medRxiv] 2025 Dec 21. Date of Electronic Publication: 2025 Dec 21. |
| Publication Type: | Journal Article; Preprint |
| Journal Info: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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