Du Haoyang, Yu Jianing, Chen Dandan, Wu Jingjie, Xue Erxu, Zhou Yufeng, Pan Xiaohua, Shao Jing, Ye Zhihong
Sir Run Run Shaw Hospital, Hangzhou, China.
School of Nursing and Institute of Nursing Research, School of Medicine, Zhejiang University, Hangzhou, China.
J Med Internet Res. 2025 Jun 5;27:e72636. doi: 10.2196/72636.
Health portraits powered by big data integrate diverse health-related data into actionable insights, thereby facilitating precise risk prediction and personalized management of noncommunicable diseases (NCDs). Despite their promise, the adoption and application of health portraits remain fragmented, primarily due to the lack of a standardized conceptual and methodological framework necessary to fully harness their capabilities.
This study aimed to systematically map and categorize existing research on health portraits in the context of NCD management, evaluate how big data has been used through the lens of the 3V (volume, velocity, and variety) framework, assess the extent of external validation and comprehensiveness, and identify challenges, emerging opportunities, and future research directions in this field.
A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and 6-step framework of Levac et al. A comprehensive search was performed in PubMed, Embase, EBSCO, Ovid, Scopus, Web of Science, and Springer Link, focusing on observational and interventional studies using big data, public databases, electronic health record systems, wearables, and sensors for NCD management from January 2014 to July 2024. Data extraction included study characteristics, modeling approaches, and external validation. Analytical synthesis was conducted using keyword analysis, the 3V framework, and visual tools such as scatter plots, heat maps, and radar charts.
A total of 8707 records were identified, and 89 studies were included for full-text analysis. These studies were categorized into 4 types of health portraits: diagnostic, prognostic, monitoring, and recommender. Evaluation based on the 3V framework showed that only 17.78% of studies met all 3 criteria. In terms of volume, structured data were widely used (64.29%-100% depending on portrait type), while unstructured data usage varied significantly (19.05%-93.33%). Regarding velocity, monitoring and recommender portraits showed high reliance on digital interactive data (over 85%). For variety, only 31.11% of studies incorporated all 3 data attributes (natural, domain, and specific attributes). In terms of comprehensiveness, only 30% of studies reported the external validation, and only 10% met both the external validation and 3V criteria, with recommender portraits outperforming the other types.
This study provides a standardized lens through which to evaluate the development and application of health portraits in NCD management. The findings underscore the need for more robust data integration strategies and emphasize the importance of artificial intelligence-enabled approaches. Furthermore, enhancing external validation and addressing ethical and privacy considerations are critical for advancing the implementation of personalized health management solutions.
由大数据驱动的健康画像将各种与健康相关的数据整合为可操作的见解，从而促进对非传染性疾病（NCDs）的精确风险预测和个性化管理。尽管它们前景广阔，但健康画像的采用和应用仍然分散，主要是由于缺乏充分发挥其能力所需的标准化概念和方法框架。
本研究旨在系统地梳理和分类非传染性疾病管理背景下关于健康画像的现有研究，从3V（体量、速度和多样性）框架的角度评估大数据的使用方式，评估外部验证的程度和全面性，并确定该领域的挑战、新出现的机会和未来的研究方向。
按照PRISMA-ScR（系统评价和Meta分析扩展版的首选报告项目）指南和Levac等人的6步框架进行范围综述。在PubMed、Embase、EBSCO、Ovid、Scopus、Web of Science和Springer Link中进行了全面搜索，重点关注2014年1月至2024年7月期间使用大数据、公共数据库、电子健康记录系统、可穿戴设备和传感器进行非传染性疾病管理的观察性和干预性研究。数据提取包括研究特征、建模方法和外部验证。使用关键词分析、3V框架以及散点图、热图和雷达图等可视化工具进行分析性综合。
共识别出8707条记录，纳入89项研究进行全文分析。这些研究被分为4种类型的健康画像：诊断型、预后型、监测型和推荐型。基于3V框架的评估表明，只有17.78%的研究满足所有3个标准。在体量方面，结构化数据被广泛使用（根据画像类型，占64.29%-100%），而非结构化数据的使用差异很大（占19.05%-93.33%）。在速度方面，监测型和推荐型画像对数字交互数据的依赖度很高（超过85%）。在多样性方面，只有31.11%的研究纳入了所有3种数据属性（自然属性、领域属性和特定属性）。在全面性方面，只有30%的研究报告了外部验证，只有10%的研究同时满足外部验证和3V标准，推荐型画像的表现优于其他类型。
本研究提供了一个标准化的视角，用于评估健康画像在非传染性疾病管理中的发展和应用。研究结果强调需要更强大的数据整合策略，并强调了人工智能方法的重要性。此外，加强外部验证以及解决伦理和隐私问题对于推进个性化健康管理解决方案的实施至关重要。
