Exploring predictors of interaction among low-birth-weight infants and their caregivers: a machine learning-based random forest approach.

ABSTRACT:

Background

Quality caregiver-infant interaction is crucial for infant growth, health, and development. Traditional methods for evaluating the quality of caregiver-infant interaction have predominantly relied on rating scales or observational techniques. However, rating scales are prone to inaccuracies, while observational techniques are resource-intensive. The utilization of easily collected medical records in conjunction with machine learning techniques offers a promising and viable strategy for accurate and efficient assessment of caregiver-infant interaction quality.

Methods

This study was conducted at a follow-up outpatient clinic at two tertiary maternal and infant health centers located in Shanghai, China. 68 caregivers and their 3-15-month-old infants were videotaped

SUBMITTER: Wang Q 

PROVIDER: S-EPMC11465835 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature