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在2型糖尿病患者中,基于机器学习的糖尿病病风险预测模型:系统性审查和元分析
Yihan Li1, Nan Jin1, Qiuzhong Zhan2
1Department of Geriatrics, Xiyuan Hospital, China Academy of Traditional Chinese Medicine, Beijing, China.
Frontiers in endocrinology
|March 18, 2025
概括
机器学习模型在预测2型糖尿病患者糖尿病病 (DKD) 风险方面表现强. 需要进一步的研究来解决数据偏差和提高模型通用性.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 医疗信息学 医疗信息学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 模型越来越多地用于预测2型糖尿病患者的糖尿病病 (DKD) 风险.
- ML模型性能的变化阻碍了广泛的临床采用.
- 进行了系统性审查和元分析,以评估ML模型的性能和适用于DKD风险预测的适用性.
研究的目的:
- 系统地审查和元分析ML模型的性能,以预测T2DM患者的DKD风险.
- 评估这些预测模型的临床适用性.
- 确定该领域的关键研究缺口.
主要方法:
- 在PubMed,Embase,Cochrane图书馆和科学网上进行了系统的文献搜索.
- 包括到2024年4月18日发表的使用ML算法用于T2DM中DKD风险预测的研究.
- 主要性能指标是接收器操作特征曲线 (AUC) 下的面积;使用PROBAST评估偏差风险.
主要成果:
- 包括26项使用94个ML模型的研究;25项使用内部验证,8项使用外部验证.
- 汇总的AUC为0.839 (95% CI 0.787-0.890) 在内部和0.830 (95% CI 0.784-0.877) 在外部验证.
- 深度学习模型实现了最高的AUC (0.863),而随机森林模型在经常使用的模型中表现最好 (AUC 0.848).
结论:
- ML模型在预测T2DM患者的DKD风险方面表现良好.
- 在模型开发和验证中的数据偏差仍然存在挑战.
- 未来的研究应该优先考虑数据透明度,标准化和多中心验证以实现通用性.
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