使用机器学习预测对行为变化支持系统的坚持:系统审查
Akon Obu Ekpezu1, Isaac Wiafe2, Harri Oinas-Kukkonen1
1Oulu Advanced Research on Service and Information Systems, Department of Information Processing Science, University of Oulu, Oulu, Finland.
JMIR AI
|June 14, 2024
概括
机器学习准确地预测用户对行为变化支持系统 (BCSSs) 的坚持. 这使得个性化干预成为可能,通过克服自我报告的坚持数据的局限性来改善健康结果.
科学领域:
- 数字健康数字健康
- 机器学习应用 机器学习应用
- 行为科学 行为科学
背景情况:
- 对于行为变化支持系统 (BCSS) 存在有限的可靠依从性预测措施.
- 现有的审查侧重于自我报告,这容易导致遵守行为的不准确性.
- 需要客观和准确的方法来预测坚持.
研究的目的:
- 系统地审查和总结用于预测遵守BCSS的机器学习 (ML) 方法.
- 识别用于粘附预测的ML应用中的趋势.
- 评估ML模型在这个领域的有效性.
主要方法:
- 在Scopus和PubMed的系统文献搜索 (2011年1月 - 2022年8月).
- 从最初的2182篇论文中包含11项符合条件的研究.
- 分析已识别的机器学习技术和遵守类别.
主要成果:
- 确定了四个遵守类别:数字干预,药物,体力活动和饮食.
- 实时依从性预测的机器学习是一个不断增长的研究领域.
- 使用了13种监督学习技术,主要是传统的 (例如,支持矢量机);先进的技术包括LSTM,多层感知和集体学习.
- 大多数模型实现了良好的分类准确性,表明了有效的特征选择.
结论:
- 机器学习算法可以预测用户在BCSS中的坚持.
- 预测模型促进了坚持行为强化.
- 智能BCSS的开发与个性化,及时的建议是通过ML实现的.
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