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一个基于机器学习的临床决策模型,用于预测糖尿病足患者的截肢风险:诊断性能和实际影响

Lei Gao¹, Zixuan Liu², Siyang Han¹

¹Orthopedic Department, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing 100038, China.

Diagnostics (Basel, Switzerland)

|December 30, 2025

概括

一个机器学习模型准确地预测了使用C反应蛋白和瓦格纳等级的糖尿病足患者的下肢截肢风险,帮助临床决策.

科学领域:

医疗信息学医疗信息学
糖尿病学糖尿病学
血管外科血管外科

背景情况:

糖尿病足 (DFU) 是下肢截肢的主要原因之一.
预测截肢风险对于及时干预和预防策略至关重要.

研究的目的:

开发可靠的机器学习 (ML) 模型,用于预测DFU患者下肢截肢风险.
为临床决策和个性化预防策略提供定量证据.

主要方法:

对149名住院的DFU患者进行了回顾性分析.
使用最小绝对收缩和选择操作员 (LASSO) 算法进行特征选择.
支持矢量机器 (SVM) 模型经过训练并通过五倍交叉验证进行验证.

主要成果:

确定C反应蛋白和瓦格纳等级是截肢的独立预测因子 (p < 0.05).
优化的SVM模型实现了ROC曲线下的面积为0.89.
模型表现出良好的预测性能,在内部验证中82.4%的正确分类.

结论:

在DFU患者中,C反应蛋白和瓦格纳等级是截肢风险的关键决定因素.
开发的SVM模型为预测截肢风险提供了强大的准确性和临床解释性.
该模型支持个性化干预和改善DFU管理的临床决策.

关键词:

在C-反应性蛋白质中.瓦格纳的分类是瓦格纳的分类.截肢是一种截肢.糖尿病足部糖尿病足部机器学习是机器学习.预测模型是一个预测模型.支持矢量机器的支持矢量机器.

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