在2型糖尿病患者12个月内对硫基尿素治疗的血糖反应预测因素:线性回归和机器学习模型的比较分析
Shilpa Garg1, Robert Kitchen2, Ramneek Gupta2,3
1Diabetes Endocrinology and Reproductive Biology, School of Medicine, University of Dundee, Nethergate, Dundee, Dundee, DD14HN, United Kingdom, 44 7443787733.
JMIR diabetes
|February 6, 2026
概括
机器学习模型在预测2型糖尿病对硫尿素药物的反应方面没有超过传统的回归. 常规临床数据可能缺乏先进模型显示显著益处所需的复杂性.
科学领域:
- 内分泌学和新陈代谢学
- 在医疗保健中的数据科学.
- 药物基因组学 药物基因组学
背景情况:
- 硫类尿素是常见的2型糖尿病治疗方法,患者的反应是可变的.
- 机器学习 (ML) 对预测建模有希望,但临床效用尚不确定.
研究的目的:
- 将ML模型的预测性能与硫氨酸尿素血糖反应的线性回归进行比较.
- 使用Shapley添加式解释 (SHAP) 评估模型的解释性.
主要方法:
- 分析了7557名2型糖尿病患者开始硫基尿素治疗.
- 训练了各种ML模型 (随机森林,XGBoost,SVM,NN,BART) 和线性回归.
- 预测的血红蛋白A1c (HbA1c) 变化和达到HbA1c<58 mmol/mol.的结果.
- 使用SHAP分析来预测因素的重要性,并在子集中分析C-.
主要成果:
- 机器学习模型的性能与线性回归相似;没有发现显著的优势.
- 贝叶斯增量回归树 (BART) 对连续的HbA1c变化具有最高的R2 (0.445).
- 极端梯度增强 (XGBoost) 在二进制结果中具有最高的AUC (0.712).
- 基线HbA1c,年龄,BMI和性别是模型中的关键预测因素.
- 较高的C-水平与更好的血糖改善相关.
结论:
- 使用常规临床数据,ML模型在预测硫基尿素反应方面没有超过传统回归.
- 从ML获得的有限收益可能源于缺乏强烈的非线性相互作用或数据中的生物学异质性不足.
- C-分析将维护的β细胞功能与改善的治疗反应联系在一起,提供了机械洞察力.
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