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可解释机器学习用于预测妊娠糖尿病中不良妊娠结果:回顾性队列研究

Jiaxi Li¹, Xiali Liu², Shenyang He¹

¹Jinniu Maternity and Child Health Hospital of Chengdu, Chengdu, China.

JMIR medical informatics

|September 17, 2025

概括

机器学习模型准确地预测妊娠糖尿病 (GDM) 的不良妊娠结果. 可解释的SHAP分析揭示了关键的风险因素,改善了母婴健康管理.

科学领域:

生殖健康生殖健康
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

妊娠期糖尿病 (GDM) 影响全球超过5%的怀孕.
GDM增加了母亲2型糖尿病的风险和不良胎儿结果,如胎儿死亡和先天异常.
有效的GDM管理对于平衡血糖控制和确保怀孕结果积极至关重要.

研究的目的:

开发可解释的机器学习模型,用于预测GDM患者的不良妊娠结果.
用Shapley增量解释 (SHAP) 算法识别不利结果的关键预测因素.
通过改进GDM风险评估和管理,增强母亲和婴儿健康.

主要方法:

进行了数据预处理和特征选择,使用适应性合成抽样来检测类不平衡.
使用堆叠方法构建和增强了分类模型 (逻辑回归,随机森林,SVM,XGBoost).
沙普利增量解释 (SHAP) 算法用于模型解释性和量化特征贡献.

主要成果:

堆叠模型在测试组中实现了85.6%的准确性和0.82的AUC,优于单个模型.
外部验证显示,性能准确率为83.6%,AUC为0.67.
SHAP分析确定了妊娠年龄,血糖控制和诊断时间作为重要的预测因素,为风险因素提供了临床相关的见解.

结论:

机器学习具有很大的潜力,可以预测GDM怀孕的不良结果.
从SHAP分析中获得的可解释特征为增强妊娠管理提供了宝贵的临床见解.
这种方法可以有助于改善GDM病例中的母婴健康结果.

关键词:

负面的怀孕结果.组合学习组合学习孕期糖尿病孕期糖尿病可以解释的模型模型.机器学习是机器学习.风险预测风险预测

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