描述饮食模式的方法的进步:一个范围审查
Joy M Hutchinson1, Amanda Raffoul2, Alexandra Pepetone1
1School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
medRxiv : the preprint server for health sciences
|July 1, 2024
概括
机器学习等新的方法为饮食模式和健康提供了更深入的见解. 对于这些先进的方法来为公共卫生政策提供信息,需要一致的报告准则.
科学领域:
- 营养流行病学 营养流行病学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 了解复杂的饮食模式及其对健康的影响至关重要.
- 传统的方法可能无法完全捕捉饮食的复杂性.
- 新的分析方法为饮食模式的表征提供了新的可能性.
研究的目的:
- 对文学 (2005-2022) 进行范围审查,以描述饮食模式的新方法.
- 综合非传统方法的应用,包括机器学习和潜在类分析.
- 确定使用这些新方法的趋势和变化.
主要方法:
- 对MEDLINE,CINAHL和Scopus数据库进行范围审查.
- 关键词包括机器学习,隐性类分析和LASSO.
- 包括标准适用于5274个已识别的记录,其中24个符合标准.
主要成果:
- 24项研究符合纳入标准,其中自2020年以来发表的12项研究.
- 研究涉及17个国家,9个国家使用机器学习应用程序.
- 14项研究将新的饮食模式与健康结果 (癌症,心血管疾病,喘) 联系起来.
结论:
- 新的方法越来越多地用于描述饮食模式,提供更深入的见解.
- 方法和报告存在显著差异,阻碍了证据综合.
- 需要加强报告准则和评估工具,以标准化营养研究中新方法的使用.
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