用人工智能 (AI) 预测长期的2型糖尿病:一个范围的审查
Salleh Sonko1, Fathima Lamya1, Mahmood Alzubaidi1
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Studies in health technology and informatics
|June 30, 2023
概括
人工智能 (AI) 在预测2型糖尿病 (T2DM) 风险方面表现有前途. 机器学习和深度学习模型,特别是组合方法,表现出高精度和回忆力,用于长期T2DM预测.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 2型糖尿病 (T2DM) 是全球普遍存在的慢性代谢障碍.
- 预测T2DM风险对于早期干预和管理至关重要.
- 人工智能 (AI) 为风险预测提供了先进的能力.
研究的目的:
- 为长期T2DM风险预测进行人工智能技术的范围审查.
- 在T2DM预测中评估不同AI模型的性能.
- 确定AI驱动的T2DM风险评估的关键验证指标.
主要方法:
- 符合PRISMA-ScR指南的范围审查方法.
- 系统地搜索和包含40项有关AI的相关研究,用于T2DM预测.
- 分析人工智能技术,包括机器学习 (ML) 和深度学习 (DL),以及它们的性能指标.
主要成果:
- 机器学习 (ML) 是最常见的AI技术 (23项研究).
- 在4项研究中使用了深度学习 (DL) 模型,在13项研究中使用了ML/DL组合模型.
- 集体学习模型是普遍存在的 (8项研究),SVM和RF是顶级的个人分类器.
- 准确性 (31项研究) 和回忆 (29项研究) 是主要的验证指标.
结论:
- 人工智能,特别是ML和DL,显示出长期T2DM风险预测的巨大潜力.
- 高预测精度和回忆对于有效的T2DM病例检测至关重要.
- 对先进的人工智能模型的进一步研究和强大的验证对于临床应用是有必要的.
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