使用机器学习来预测妊娠糖尿病的药理疗法:一个范围审查
Jasmine R Kirkwood1, Natasha Galloway2, Robert S Lindsay3
1Centre for Cardiovascular Science, Queen's Medical Research Institute, The University of Edinburgh, Edinburgh, UK.
Diabetic medicine : a journal of the British Diabetic Association
|November 19, 2025
概括
机器学习模型可以预测妊娠糖尿病 (GDM) 中需要药理疗法的需要. 然而,很少有模型得到外部验证或在临床上适用,这限制了它们在现实世界中的使用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 生殖医学 生殖医学
背景情况:
- 孕期糖尿病 (GDM) 是一种常见的妊娠并发症,需要及时管理.
- 药物治疗对于GDM通常是必要的,需要早期识别.
- 机器学习 (ML) 提供了预测治疗需求的潜力.
研究的目的:
- 进行对预测GDM药理疗法的ML模型的范围审查.
- 确定在GDM管理中使用的常见ML方法和预测因素.
- 评估这些模型的验证和临床适用性.
主要方法:
- 搜索了Embase,Medline,IEEE Xplore和Web of Science数据库 (2007年7月至2024年8月) 的数据库.
- 包括预测使用ML对GDM进行药理疗法的研究.
- 使用乔安娜·布里格斯研究所和PRISMA-ScR检查清单和PROBAST工具评估研究质量.
主要成果:
- 审查了17项44个ML模型的研究;大多数预测的一般药理疗法 (61.4%) 或胰岛素使用 (38.6%).
- 后勤回归是常见的,预测因素包括GDM史,妊娠周,BMI,孕妇年龄和葡萄糖水平.
- 接收器操作曲线下的中位面积为0.75;65.9%的模型得到了验证,但外部验证很少.
结论:
- 使用临床变量的后勤回归模型经常预测GDM药理疗法.
- 有限的外部验证和缺乏临床应用阻碍了这些预测模型的广泛使用.
- 需要进一步的研究来改善GDM管理的模型通用性和临床整合.
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