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解释机器学习模型来预测糖尿病足患者的截肢风险:一个多中心研究
Haoran Tao1,2, Lili You1,2, Yuhan Huang3
1Department of Endocrinology, Sun Yat-Sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Frontiers in endocrinology
|April 9, 2025
概括
机器学习模型可以预测糖尿病足患者的下肢截肢 (LEA) 风险. XGBoost模型显示出高准确度,识别了防止截肢的关键预测因素.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 糖尿病学 糖尿病学
背景情况:
- 糖尿病足 (DFU) 是糖尿病患者下肢截肢 (LEA) 的主要原因.
- 预测LEA风险对于及时干预和肢体救援至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测DFU患者的LEA风险.
- 使用夏普利增量解释 (SHAPs) 识别LEA的关键预测因素.
主要方法:
- 这是一项回顾性研究,使用了1,332名DFU患者的数据 (1,035名内部,297名外部验证).
- 开发并比较了五种ML模型:物流回归,SVM,随机森林,KNN和XGBoost.
- 使用SHAP分析来解释特征和最小绝对收缩,以及选择操作员分析来识别预测因素.
主要成果:
- XGBoost模型表现出卓越的性能,AUC为0.93 (内部) 和0.83 (外部验证).
- 通过SHAP分析发现的关键预测因素包括白细胞计数,淋巴细胞计数和血尿素水平.
- 该模型实现了高精度 (0.94内部,0.78外部) 和精度 (0.96内部,0.93外部).
结论:
- 基于XGBoost的预测模型为动态评估DFU患者LEA风险提供了有价值的工具.
- 这种预测能力可以帮助防止DFU的进展到截肢.
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