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国内大数据与膳食营养健康的研究及应用进展

泰然健康网
2024-12-01 19:01

摘要: 大数据是一种新兴的信息技术，广泛应用于各行各业。大数据技术在膳食营养健康中的应用主要是结合营养学、医学等相关学科，利用大数据技术挖掘、分析数据，再根据个人生理指标提供个性化的信息服务。本文以Web of Science核心合集和中国知网中文数据库为数据来源，利用VOSviewer，对近10年大数据与膳食营养健康领域的相关文献进行年度发文量和关键词分析。总结分析了大数据技术在膳食营养健康领域中的应用以及其所面临的挑战。最后，对大数据在膳食营养健康领域的未来研究趋势做出了展望，以期为大数据技术在该领域的深入研究及应用提供帮助。

Abstract: As an emerging information technology, big data has been widely used in all aspects of life. As for the application of big data technology in dietary nutrition and health, it revolves mainly around the combination of nutrition, medicine and other relevant disciplines, during which big data technology is adopted to mine and analyze data for the provision of personalized information services tailored to personal physiological indicators. With the core collection of Web of Science and CNKI Chinese database as data sources, and VOSviewer is applied in this paper to analyze the annual volume of publication and the keywords of relevant literature on big data as well as dietary nutrition and health published over the past 10 years. Then, the application of big data technology in the field of dietary nutrition and health and the challenges arising from this field are summarized and analyzed. Finally, the future trend of big data research in the field of dietary nutrition and health is indicated, which would provide guidance for the in-depth research and application of big data technology in this field.

图 1 2011~2021年大数据在膳食营养健康领域研究的文献数量图

Figure 1. Distribution of the number of literatures on big data in the field of dietary nutrition and health from 2011 to 2021

图 2 CNKI关键词共现网络图

Figure 2. CNKI keyword co-occurrence network diagram

图 3 WOS关键词共现网络图

Figure 3. WOS keyword co-occurrence network diagram

表 1 2011~2021年间大数据在膳食营养健康相关研究关键词共现聚类簇

Table 1 Co-occurrence clusters of big data in dietary nutrition and health related research from 2011 to 2021

聚类编号 CNKI关键词聚类簇
聚类标签WOS关键词聚类簇
聚类标签 1hadoop、应用研究、数据挖掘、
智慧医疗、糖尿病、饮食管理genetic testing, high-fat diet, metabotying, nutrition,
personalized nutrition, precision nutrition, vitamins
2亚健康、健康管理、大数据技术、
大数据时代、慢性病3 d food printing, 3 d printing, customized fabrication,
muti-material, platform design, printing precision
3个性化营养、慢性病预防和管理、
精准营养、营养基因competitive sports, dietary nutrition, nutrition supplement,
nutrition status, reasonable diet4个性化推荐、推荐系统、
机器学习、深度学习allergic diseases,
metabolic intervention,
pregnancy5云计算、慢性病管理、物联网body mass index, enterotype,
gut microbiota, short-chain fatty acids6云平台、互联网+、营养健康diet, metabolomics,
obesity, proteomics7信息化平台、大数据、慢病管理fatty acids, nutrigenomics8体质健康、大数据平台、学生9营养、营养管理、食品安全10健康领域、大数据背景、食品营养 11隐私保护12管理系统

表 2 国外食物成分数据库相关信息

Table 2 Relevant information of foreign food composition databases

数据库名称数据库内容用途阿拉伯食物成分数据库[35]数据库开发为在线膳食评估工具myfood24；包含2016种食物及其大量营养素、微量营养素数据
旨在促进营养流行病学研究，帮助测量阿拉伯人口的
饮食摄入
爱尔兰食物成分数据库[36]共编入938种食品，包括459种复合菜肴、92种营养补充剂促进数据共享；为研究人员和决策者提供爱尔兰特定
食品成分数据
DietSys[37]包含9851种食品，其中5901种被分配为1至27份或者部分大小的选择，41% 的食品被归类为超加工食品用于高效、准确的饮食分析；用于评估巴西、阿根廷和
北美居民的饮食摄入量myfood24 FCDB[38]
开发为在线膳食评估工myfood24；包含40274常用食品和品牌食品，包含120个大量营养素、微量营养素数据，5669个食物部分图像
反映英国消费者可获得的各种食品，并潜在地
提高饮食评估的准确性喀里多尼亚食物成分
数据库[39]数据库集成到基于移动应用程序的24 h召回工具iRecall中，共有972种食品，涵盖40个食品类别和25种营养价值，包括饱和脂肪和总糖对食物和营养数据进行更改提供了灵活性；为新喀里多尼亚的居民制定有针对性的公共卫生政策和个人及社区一级的营养干预，以改善饮食，减少与饮食有关的健康风险

表 3 营养健康服务平台信息

Table 3 Information on nutrition and health service platform

平台名称结构框架服务对象目的食品安全与营养云服务平台架构方案[41]数据汇聚层、数据处理层、信息分析层、用户应用层
服务于政府、企业、
消费者、社会媒体实现数据收集、数据处理以及信息服务膳食营养云服务
平台[42]基础架构即服务、平台即服务和软件即服务，不同云层
提供不同的服务
一般人群、餐饮企业、专业营养师以及公共卫生政府部门传播营养学及慢性病预防知识；获取人群膳食信息将相关统计数据快速传递给政府后指导民众营养均衡、科学膳食；为政府制定
相关营养政策提供参考依据
营养健康知识服务
系统平台[43]知识资源仓储层、知识服务支撑层、知识服务用户层专家、一般人群打造符合国家营养健康产业发展需要的营养健康知识资源汇聚中心和综合性知识服务平台；探索和构建营养健康领域知识服务模式

表 4 三种疾病监测系统信息

Table 4 Information of three disease surveillance systems

名称框架用途高血压早期预警和
健康管理平台[64]数据采集和存储模块、健康风险分析模块、健康指导模块、健康管理
效果评价模块
利用机器学习的数学模型进行健康风险评估，采用大数据的Hadoop平台。对与影响
高血压的有关因素（摄盐量、摄油量等）进行监测后，由专家给出健康指导意见，
提高患者的知晓率、治疗率智能体重管控系统[65]摄入量模块、输出量模块、个人体重数据、群体体重数据、预警模块依托大数据提供专业、实时、高效的智能系统。对个人数据进行长期收集，将个人数据与群体数据进行比对，对用户在使用过程中的营养素摄入量、热量、运动量等直接与
健康情况相关的各项指标进行监测，实时发出预警
糖尿病患者膳食
管理系统[66]用户层、应用层、数据层和支持层4层能有效辅助和优化糖尿病患者临床饮食治疗工作；利用食物互换法编制饮食方案，
能科学有效地满足患者膳食的个性化需求；为患者、医护人员及管理者之间交流互动
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