机器学习的类型,功能和机制,用于个性化戒烟干预措施:系统范围审查
Yu Jie Xavia Ng1, Shing Hui Reina Cheong2, Wen Wei Ang3
1HCA Hospice Limited, Singapore.
机器学习 (ML) 通过信息定制,事件预测和生物标志物分析来个性化戒烟干预措施. 需要进一步的研究来验证ML模型对帮助人们戒烟的有效性.
科学领域:
- 人工智能在公共卫生中的作用
- 行为改变的计算方法.
背景情况:
- 个性化是有效戒烟干预措施的关键.
- 机器学习 (ML) 为定制健康行为提供了先进的功能.
研究的目的:
- 系统地审查用于个性化戒烟干预的ML类型,功能和机制.
- 为了未来的发展,识别当前研究中的差距和局限性.
主要方法:
- 从14个数据库中对98篇文章进行了系统的范围审查.
- 两名独立审稿人使用标准化表格提取数据.
主要成果:
- 监督学习是最常见的ML技术 (81%).
- 关键的ML功能包括吸烟事件的预测/检测 (34%),预测建模 (24%) 和消息定制 (17%).
- ML机制涉及数据输入,预处理,特征选择,培训,验证和输出.
结论:
- 本综述强调了ML在个性化戒烟方面的新兴但显著的潜力.
- 未来的研究必须专注于改进,验证和实验测试ML模型以证明其有效性.
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