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对基于人工智能的糖尿病风险预测方法进行范围审查
Farida Mohsen1, Hamada R H Al-Absi1, Noha A Yousri2,3,4
1College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
NPJ digital medicine
|October 25, 2023
概括
人工智能 (AI) 模型在预测2型糖尿病 (T2DM) 风险方面表现有前途. 使用电子健康记录和多omics数据的多式人工智能方法优越,但需要更多的外部验证和可解释性用于临床使用.
科学领域:
- 计算医学和生物信息学
- 医疗保健中的人工智能
- 糖尿病研究研究 糖尿病研究
背景情况:
- 随着2型糖尿病 (T2DM) 患病率的上升,需要先进的预测建模来进行早期诊断和干预.
- 现有的人工智能 (AI) 模型用于T2DM风险预测缺乏对其进展和局限性的全面审查.
研究的目的:
- 进行范围审查,绘制T2DM预测基于AI模型的格局.
- 识别用于T2DM风险评估的人工智能的进展,数据模式,验证策略和挑战.
主要方法:
- 在PubMed,Scopus,IEEE-Xplore和Google Scholar中进行系统的文献搜索,遵循PRISMA-ScR指南.
- 对40项侧重于人工智能模型用于T2DM预测的纵向研究的综述.
- 数据模式 (EHR,多omics,成像) 的分析,AI模型类型 (单模与多模),验证和可解释性.
主要成果:
- 经典的机器学习模型占主导地位,主要使用电子健康记录 (EHR) 和多omics数据.
- 多模人工智能模型在单模方法上表现出更高的性能,尽管使用它们的研究较少.
- 报告了有限的外部验证和模型校准见解;在一半的研究中使用了可解释性方法.
结论:
- 人工智能模型,特别是多式联运模型,为T2DM风险预测提供了显著的潜力.
- 关键的挑战包括需要强大的外部验证,改进的校准和提高临床整合的可解释性.
- 进一步的研究应侧重于解决这些局限性,以将人工智能进步转化为T2DM管理的有效临床实践.
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