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通过基于归算的机器学习框架提高早期妊娠糖尿病预测:对现实世界的临床记录进行比较研究

Leyao Ma¹, Lin Yang^1,2, Yaxin Wang³

¹Institute of Medical Information, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.

|August 4, 2025

概括

推算方法显著改善使用电子健康记录 (EHR) 的妊娠糖尿病 (GDM) 预测模型. 通过链式方程 (MICE) 的多变量归算提高了模型性能和可靠性,用于早期GDM风险评估.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
生殖健康生殖健康

背景情况:

孕期糖尿病 (GDM) 是一种常见的妊娠并发症.
电子健康记录 (EHR) 提供了GDM风险预测的潜力.
电子健康记录中缺少的数据阻碍了可靠预测模型的开发.

研究的目的:

在早期GDM预测中解决缺失的EHR数据挑战.
评估归算方法对GDM风险预测模型的影响.
确定临床机器学习管道的最佳归算策略.

主要方法:

对5066名单独怀孕的妇女进行了回顾性研究.
评估6种归算方法与4种机器学习模型相结合.
使用十倍交叉验证评估预测性能,稳定性,数据分布恢复和功能选择.

主要成果:

推算显著改善了GDM预测模型的性能.
使用MICE的后勤回归实现了最高的AUC (0.6899) 与没有归算 (0.6336) 相比.
MICE表现出优越的平均性能,数据分布恢复,以及对失踪的稳定性.

结论:

推算对于提高GDM预测模型的性能和公平性至关重要.
结果为将归算纳入临床EHR数据分析提供了实际指导.
在中国人口中确定了早期GDM预测的18个关键特征.

关键词:

孕期糖尿病是一种糖尿病.电子健康记录是电子医疗记录.归算是指指责一个人.机器学习是机器学习.缺失的数据缺失的数据风险预测风险预测

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