在亚洲女性中使用机器学习算法预测妊娠糖尿病
Byung Soo Kang1, Seon Ui Lee2, Subeen Hong1
1Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Scientific reports
|August 16, 2023
概括
这项研究开发了一种机器学习算法,用于预测妊娠糖尿病 (GDM) 风险. 该模型有效地使用母亲因素和实验室数据,在预测GDM方面达到高准确性.
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) 对母亲和胎儿都有风险.
- 准确预测GDM对于及时干预和改善结果至关重要.
- 现有的预测模型可能无法充分利用复杂的数据集.
研究的目的:
- 开发和验证用于预测GDM风险的机器学习算法.
- 为了比较不同机器学习模型和变量选择方法的性能.
- 创建基于关键预测因素的简化预测工具.
主要方法:
- 韩国34,387例多中心怀孕的回顾性分析.
- 开发和比较轻度梯度增强机 (LGBM) 和极度梯度增强 (XGBoost) 算法.
- 使用临床指南,SHAP值和Boruta算法进行变量选择.
主要成果:
- 机器学习模型表现出良好的预测性能,在M1时AUC值高达0.804.
- 博鲁塔算法确定了一组最小的变量,这些变量产生了可比或优异的预测性能.
- 基于最有效的变量选择,开发了一个简单的问卷模型.
结论:
- 机器学习模型有效地使用母体和实验室数据预测个体GDM风险.
- 使用特征选择技术可以开发一个节的预测模型.
- 开发的模型具有早期GDM风险评估和管理的潜力.
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