From mother to infant: predicting infant temperament using maternal mental health measures and tabular machine learning models
These findings highlight the potential of ML tools in early identification of at-risk infants and the importance of integrating MMH screening into postnatal care. Such predictive insights can inform timely, personalized interventions that address the unique emotional needs of both mother and infant, ultimately fostering healthier developmental trajectories and enhancing overall family well being.
Authors
- Rawan AlSaad (ORCID: https://orcid.org/0000-0002-3235-0860)
- Robert Thomas (ORCID: https://orcid.org/0000-0001-8866-0684)
- Aliya Tabassum
- Javaid I. Sheikh (ORCID: https://orcid.org/0000-0002-5762-4186)
- Majid Alabdulla (ORCID: https://orcid.org/0000-0002-3631-2650)
Institutions
- Weill Cornell Medical College in Qatar (QA)
- Hamad Medical Corporation (QA)
- Qatar University (QA)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2025-09-18
- DOI
- https://doi.org/10.3389/fpubh.2025.1659987
- Citations
- 2
- Primary Topic
- Maternal Mental Health During Pregnancy and Postpartum
- Type
- article
- Field-Weighted Citation Impact
- 3.76