机器学习在幼儿发展研究中的应用:一个范围审查
Faith Neema Benson1,2, Daisy Chelangat3, Willie Brink2
1Institute for Human Development, The Aga Khan University, Nairobi, Kenya faith.neema@aku.edu.
BMJ open
|August 19, 2025
概括
机器学习 (ML) 对早期儿童发展 (ECD) 研究有前途,但目前的研究缺乏全球多样性和现实世界的应用. 未来的努力应优先考虑包容性,可解释性和可验证的模型进行实际实施.
科学领域:
- 计算科学是一种计算科学.
- 发展心理学是发展心理学.
- 儿科 儿科 儿科
背景情况:
- 儿童早期发展 (ECD) 对于终身福祉至关重要,但数百万儿童面临发展缺陷的风险.
- 传统的评估方法可能无法充分捕捉发展风险.
- 机器学习 (ML) 提供了一种新的方法来分析复杂的数据并识别微妙的发展模式.
研究的目的:
- 系统地审查和绘制关于在ECD研究中ML应用的现有文献.
- 为了确定地理分布,研究差距,以及未来的研究方向在ML为ECD.
- 分析应用的ML技术,数据类型,特征集,结果和验证策略.
主要方法:
- 使用Arksey和O'Malley框架进行了范围审查.
- 在主要的科学数据库 (PubMed, Web of Science, IEEE Xplore, PsycINFO) 和灰色文献中进行了系统的搜索.
- 包括将ML或其变体应用于0-8岁儿童发育结果的研究,但对某些疾病和综述文章有特定的排除.
主要成果:
- 27项研究符合纳入标准,主要来自高收入国家 (78%),撒哈拉以南非洲没有代表.
- 监督的ML分类器 (40.7%) 和深度学习 (22.2%) 是常见的,主要针对认知发展 (33.3%).
- 大多数研究都专注于预测 (74.1%),经常使用图像,视频和传感器数据,但缺乏外部验证 (7.4%) 和可解释性方法 (41%).
结论:
- 人们对ECD的ML越来越感兴趣,但在地理多样性,外部验证和实际实施方面存在重大差距.
- 未来的研究必须优先发展具有包容性,可解释性和外部验证的ML模型.
- 将这些先进的模型集成到现实环境中,对于改善全球ECD结果至关重要.
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