如何使用机器学习和模糊认知地图来测试健康行为改变干预中的假设场景:关于水果摄入的案例研究
Samvel Mkhitaryan1, Philippe J Giabbanelli2, Maciej K Wozniak3
1Department of Health Promotion, CAPHRI, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands. mkhitarian.samvel@gmail.com.
BMC public health
|December 12, 2023
概括
机器学习和模糊的认知地图可以改善健康行为干预. 这种计算方法有助于干预者通过测试复杂逻辑模型的场景来改进更高效率的策略.
科学领域:
- 计算式健康科学 计算式健康科学
- 行为科学和公共卫生
背景情况:
- 逻辑模型对于设计基于证据的健康行为干预措施至关重要.
- 复杂的健康行为需要先进的计算技术来设计有效的干预措施.
研究的目的:
- 展示机器学习 (ML) 和模糊认知地图 (FCM) 如何帮助设计健康行为改变干预措施.
- 利用现实世界干预的经验数据来验证ML-FCM方法.
主要方法:
- 对257名荷兰成年人关于水果摄入量的15个决定因素的纵向数据应用了修改后的实编码遗传算法.
- 模糊的认知地图被用来分析两种专家设计的假设干预场景的影响.
主要成果:
- 模拟表明,假设的干预措施对水果摄入的影响有限.
- 这些模拟结果与干预研究中的经验证据一致.
结论:
- 机器学习与模糊的认知地图相结合,为使用复杂的逻辑模型开发健康行为干预提供了一个强大的工具.
- 测试假设的干预场景可以帮助干预者优化组件,以提高潜在的有效性.
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