营养推系统的地图绘制:来自同行评审的研究,应用程序和专利的证据
Kai Zhao1,2,3, Xinyu Xue1,2,3, Ningsu Chen1,2,3
1Clinical Epidemiology and Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Nutrition reviews
|January 12, 2026
概括
营养推系统 (NRS) 提供个性化的建议,但往往缺乏真正的个性化和专家投入. 未来的系统需要更好的数据,更新的算法和临床使用的协作.
科学领域:
- 数字健康数字健康
- 营养科学 营养科学
- 人与计算机的交互
背景情况:
- 营养推系统 (NRS) 将用户数据与营养知识相结合,提供个性化的建议.
- 尽管它们充满希望,但NRS在设计,实施和临床适用性方面面临挑战.
- 需要全面的概述来绘制NRS发展,特征和识别差距.
研究的目的:
- 系统地审查NRS的发展,特性和技术方面.
- 确定在NRS的应用和评估中存在的差距.
- 提供包括同行评审和非同行评审来源在内的广泛概述.
主要方法:
- 系统地搜索文献数据库,专利存储库和软件商店.
- 提取关于出版年份,主题,用户,输入,输出和目标人群的数据.
- 详细的数据收集对同行评审的NRS关于关联,系统特征,评估,人工智能和算法.
主要成果:
- 878个NRS被确定;43.4%在2022年后发布,主要针对普通或超重人群.
- 大多数系统使用一般信息,缺乏营养学家的参与 (4/49),并依赖未经验证的公众调查.
- 关注营养建议 (53.1%),通过内部测试进行评估 (22.4%),准确度指标 (34.7%),CNN/随机森林 (14.3%) 和过时的基于内容的过 (30.6%).
结论:
- 目前的NRS表现出有限的个性化,标准化的评估和专家投入.
- 依赖一般数据和过时的算法阻碍了临床相关性.
- 提高个性化,数据透明度和跨学科合作对于未来的NRS有效性至关重要.
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