Exploring predictors of interaction among low-birth-weight infants and their caregivers: a machine learning–based random forest approach
The proposed method presents a promising and efficient approach that synergistically combines rating scales and artificial technology to detect important features of caregiver-infant interactions. This novel approach holds several implications for the development of automatic computational assessment tools in the field of nursing studies.
Authors
- Wenying Gao (ORCID: https://orcid.org/0000-0002-8710-5627)
- Zijin Ren (ORCID: https://orcid.org/0000-0003-3241-0568)
- Qihui Wang (ORCID: https://orcid.org/0000-0003-3768-0401)
- Yi Duan (ORCID: https://orcid.org/0000-0001-9544-7961)
- Ying Zhang (ORCID: https://orcid.org/0000-0001-5476-1524)
Institutions
- Shanghai Jiao Tong University (CN)
- Shanghai First Maternity and Infant Hospital (CN)
Publication Details
- Journal
- BMC Pediatrics
- Published
- 2024-10-10
- DOI
- https://doi.org/10.1186/s12887-024-05080-8
- Citations
- 1
- Primary Topic
- Infant Development and Preterm Care
- Type
- article
- Field-Weighted Citation Impact
- 1.11
Funders
- Shanghai Municipal Health Commission
- Shanghai Jiao Tong University
- School of Medicine, Shanghai Jiao Tong University