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基于可解释的模型和名ogram的基础上,开发下肢形在遗传性多重外位症患者的预测模型

Rongbin Lu¹, He Ling², Zhao Huang¹

¹Department Orthopedics Trauma and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, NO. 6 ShuangYong Road, Nanning, Guangxi, 530022, China.

BMC medical informatics and decision making

|September 30, 2025

概括

像白蛋白 (ALB),血球蛋白 (GLB) 和血活性 (PA) 这样的血液测试标志物在遗传性多重异位症 (HME) 患者中预测了真实选择性. GLB,PA和尿酸 (UA) 预测脚,有助于早期风险评估.

科学领域:

整形外科整形外科整形外科
生物化学生物化学
医疗信息学医疗信息学

背景情况:

遗传性多重排骨症 (HME) 是一种遗传性疾病,其特征是骨过度生长.
下肢形,如骨和脚,是HME患者常见的并发症.
早期预测这些形对于及时干预和管理至关重要.

研究的目的:

为了确定血液检测的关键因素,可以预测HME患者的 genu valgum和 ankle valgus.
开发一种可解释的机器学习模型来进行风险评估.
构建一个名图,用于预测HME下肢形.

主要方法:

对140名HME患者的血液检测结果和临床数据的回顾性分析.
五种机器学习模型的比较,以选择最佳的预测模型.
多变量回归分析和SHAP分析以确定独立预测因素.
使用R和JD_DCPM软件开发一个预测名ogram.

主要成果:

随机森林模型有效地预测了HME患者的真和脚.
血活动 (PA) 显著促进了骨和脚.
真人黄金的独立预测因素包括白蛋白 (ALB),血球蛋白 (GLB) 和PA.
关节的独立预测因素包括GLB,PA和尿酸 (UA).
开发的名图表显示了良好的预测性能.

结论:

在HME患者中,ALB,GLB和PA是真实 valgum的显著预测因素.
在HME患者中,GLB,PA和UA是关节的显著预测因素.
可解释的随机森林模型和名图使HME下肢形的早期风险评估更容易,可能改善患者的治疗结果.

关键词:

关节瓦尔古斯关节瓦尔古斯这是一个真正的选择.在HME中,HME是HME.可以解释的模型这个名字叫做Nomogram.

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