预测从妊娠糖尿病过渡到2型糖尿病的机器学习:系统性审查
Nisrin Magboul Elfadel Magboul1, Nagla Osman Mohamed Dkeen1, Hiba Abdelraouf Hyder Mohammed2
1Obstetrics and Gynecology, Najran Armed Forces Hospital, Ministry of Defense Health Services, Najran, SAU.
Cureus
|June 19, 2025
概括
机器学习模型在预测妊娠糖尿病 (GDM) 后的2型糖尿病 (T2D) 风险方面表现有希望. 整合omics数据可以提高准确性,但在T2D预防中临床使用需要强有力的验证.
科学领域:
- 生殖内分泌学和新陈代谢
- 计算生物学和机器学习
背景情况:
- 孕期糖尿病 (GDM) 是产后2型糖尿病 (T2D) 的重要危险因素.
- 早期识别高风险妇女对于有针对性的干预和改善孕产妇健康结果至关重要.
- 机器学习 (ML) 提供了开发T2D过渡后GDM预测模型的潜力.
研究的目的:
- 系统地审查和评估ML模型的性能,预测特征和方法质量,以预测有史以来GDM的女性T2D风险.
- 确定现有ML模型中的关键特征并评估验证方法的稳定性.
主要方法:
- 在主要的科学数据库 (PubMed,Scopus,IEEE Xplore,Web of Science) 中进行了全面的系统文献搜索.
- 在选了178个记录后,包括了13项研究,并提取了研究特征,ML算法,预测特征,性能和验证的数据.
- 使用PROBAST工具评估了偏差风险.
主要成果:
- ML模型显示了可变的性能,曲线下的面积 (AUC) 从0.72到0.92.9不等.
- 与仅使用临床数据的模型相比,结合omics数据的模型显示出更高的性能.
- 主要预测特征包括年龄,体重指数 (BMI),血糖指数和妊娠特异因素;然而,只有38%的研究使用了强大的外部验证.
结论:
- 机器学习模型,特别是集成omics数据的机器学习模型,对于预测GDM后T2D风险具有重大潜力.
- 验证方法的异质性和有限的外部验证需要标准化的报告和更大,更多样化的队列.
- 未来的研究应该专注于开发可重复和通用的ML模型,用于个性化的T2D预防策略.
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