机器学习模型的开发,用于预测非糖尿病胃癌患者的术后高血糖症:回顾性队列研究分析
Nan Wang1,2, Jie Zhang1,2, Chaonan Fei3
1School of Nursing, Nanjing University of Chinese Medicine, Nanjing, China.
Frontiers in endocrinology
|November 26, 2025
概括
这项研究开发了一种机器学习模型,用于预测非糖尿病胃癌患者的术后高血糖症 (POH). SVM-辐射模型准确地识别了高风险个体,以进行个性化的血糖管理.
科学领域:
- 在瘤学瘤学.
- 内分泌学 在内分泌学.
- 医疗信息学 医疗信息学
背景情况:
- 手术后高血糖 (POH) 是胃癌手术后非糖尿病患者的常见并发症,增加了不良结果.
- 由于变量有限和传统的统计方法,POH现有的预测模型往往缺乏准确性和通用性.
研究的目的:
- 开发和验证用于早期预测POH风险的机器学习 (ML) 模型.
- 在接受激进胃切除术的非糖尿病患者中确定POH的关键外科预测因子.
主要方法:
- 一项追溯的队列研究,涉及393名经过激进胃切除术的非糖尿病患者.
- 收集了38个术后临床特征,并比较了9个ML算法,包括SVM-radial.
- 评估模型使用AUC,准确性,F1得分和SHAP分析来确定特征的重要性.
主要成果:
- POH发生率为42.7%.
- SVM-辐射模型表现出卓越的性能 (AUC=0.758),具有出色的区分和校准.
- 确定的主要预测因素包括手术持续时间,营养风险评分,性别,机器人手术,手术前葡萄糖,血栓形成风险评分和性酸酶.
结论:
- 一个基于ML的新型模型使用多维外科手术数据准确预测非糖尿病胃癌患者的POH风险.
- SVM辐射模型提供了卓越的预测性能和可解释性,有助于早期风险分层和个性化血糖管理.
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