Predicting Infant Sleep Patterns From Postpartum Maternal Mental Health Measures: Machine Learning Approach
ML models can accurately predict which infants are at risk for suboptimal sleep based on MMH measures, enabling personalized, responsive, and developmentally informed postpartum care that promotes long-term maternal and infant well-being.
Authors
- Rawan AlSaad (ORCID: https://orcid.org/0000-0002-3235-0860)
- Rajat M. Thomas (ORCID: https://orcid.org/0000-0002-5362-4816)
- Raghad Burjaq
- Javaid I. Sheikh (ORCID: https://orcid.org/0000-0002-5762-4186)
- Majid Alabdulla (ORCID: https://orcid.org/0000-0002-3631-2650)
- Alaa Abd‐Alrazaq (ORCID: https://orcid.org/0000-0001-7695-4626)
Institutions
- Weill Cornell Medical College in Qatar (QA)
- Hamad Medical Corporation (QA)
- Qatar University (QA)
Publication Details
- Journal
- JMIR Pediatrics and Parenting
- Published
- 2026-02-03
- DOI
- https://doi.org/10.2196/78937
- Primary Topic
- Maternal Mental Health During Pregnancy and Postpartum
- Type
- article
- Field-Weighted Citation Impact
- 0.00