人工智能方法在孕期糖尿病预测中:一个系统的文献综述
Valentina Ivanovic1, Md Abu Jafar Sujan2, Ole Jakob Mengshoel1
1Department of Computer Science, Norwegian University of Science and Technology, Sem Sælands vei 9, Trondheim, 7034, Norway.
International journal of medical informatics
|November 14, 2025
概括
人工智能 (AI) 和机器学习 (ML) 显示出预测妊娠糖尿病 (GDM) 的前景. 本综述强调了常见的AI方法及其性能,强调了在GDM预测中需要更好的验证和可解释的模型.
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:
- 孕期糖尿病 (GDM) 是一种普遍存在的妊娠并发症,具有显著的孕产妇和新生儿健康风险.
- 早期和个性化的GDM预测对于及时干预和改善结果至关重要.
- 人工智能 (AI) 和机器学习 (ML) 在GDM预测准确性和个性化方面提供了潜在的进步.
研究的目的:
- 系统地审查和概述用于GDM预测的AI/ML方法.
- 分析用于GDM预测的AI/ML模型中预先怀孕和怀孕数据的利用.
- 评估AI/ML研究在GDM预测中的质量和报告标准.
主要方法:
- 在主要的科学数据库 (PubMed,Scopus,IEEE,Web of Science) 进行了PRISMA引导的系统搜索,截至2024年5月27日.
- 包括的研究应用AI/ML用于GDM预测,并经过同行评审.
- 数据提取涵盖了30个维度,使用PROBAST和IJMEDI检查清单进行质量评估.
主要成果:
- 78项研究符合纳入标准,其中逻辑回归,基于树的模型和支持向量机器是最常见的.
- 神经网络经常被报告为表现最好,其次是增强和基于树的方法.
- 许多研究缺乏外部验证,对临床指标的透明报告,以及可使用的可解释AI.
结论:
- 基于AI/ML的GDM预测正在进步,为更早,个性化的干预提供了潜力.
- 需要提高报告透明度,外部验证和开发可靠,可解释的AI模型.
- 数据科学家,临床医生和医疗保健系统之间的加强合作对于将AI创新转化为临床实践至关重要.
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