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.
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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        Value: 10.1101/2025.10.13.25337771
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