机器学习和人工智能在2型糖尿病的预测:一个全面的33年文献分析和文献分析
Mahreen Kiran1, Ying Xie2, Nasreen Anjum3
1Faculty of Health, Medicine and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom.
Frontiers in digital health
|April 11, 2025
概括
机器学习和人工智能在预测2型糖尿病 (T2DM) 方面呈现快速增长. 未来的研究应该专注于有效的糖尿病管理的概括性和临床应用.
科学领域:
- * 计算健康信息学 计算健康信息学
- * 生物医学数据科学
- * 预测分析 预测分析
背景情况:
- *2型糖尿病 (T2DM) 是一个重大的全球健康挑战.
- *对T2DM的早期发现和个性化干预至关重要.
- *机器学习 (ML) 和人工智能 (AI) 为T2DM预测提供了有前途的工具.
研究的目的:
- *对T2DM预测中的ML和AI应用进行全面的文献计量和系统审查.
- *分析33年 (1991-2024) 的研究趋势,方法和发现差距.
- *强调AI/ML在T2DM研究中的演变和影响.
主要方法:
- *使用PRISMA指南在Web of Science和Scopus数据库中进行系统的文献搜索.
- *使用VOSviewer和Bibliometrix进行图书统计分析,用于主题聚类和网络可视化.
- * 结合相关性和引用指标的双重标准方法论,用于具有影响力的文献选择.
主要成果:
- *自2010年以来出版物的指数增长,美国和英国是主要贡献者.
- * 集合方法 (例如,随机森林) 和深度学习 (例如,CNNs) 在最近的进展中占主导地位.
- * 整合各种预测因素 (遗传,生活方式) 和新兴技术,如联合学习和可解释性工具 (SHAP,LIME).
结论:
- * 确定的研究差距包括可概括性,跨学科方法和心理社会因素.
- *强调需要临床可行的解决方案和现实世界的应用性.
- *强调了AI/ML的潜力,以有效打击日益增长的糖尿病流行病.
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