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在患有全身性红斑狼的患者中,认知功能障碍的预测模型
Wenjuan Bian1,2,3, Yuxuan Zhu3, Yantong Zhu3
1School of Nursing, Nanjing Medical University, No.101 Longmian Ave., Nanjing, 211166, Jiangsu, China.
Clinical rheumatology
|January 22, 2026
概括
这项研究开发了一种机器学习模型,用于预测系统性红斑狼 (SLE) 患者的认知功能障碍 (CD). 该模型有助于在SLE患者中早期发现和干预CD.
科学领域:
- 风湿病学和免疫学
- 计算医学是一种计算医学.
- 神经科学是一个神经科学.
背景情况:
- 认知功能障碍 (CD) 是系统性红斑狼 (SLE) 的普遍且经常被忽视的并发症.
- CD显著降低了SLE患者的生活质量.
- 早期发现和预测CD对于及时干预至关重要.
研究的目的:
- 开发和验证系统性红斑狼 (SLE) 患者认知功能障碍 (CD) 的预测模型.
- 评估开发模型的预测效率和临床适用性.
主要方法:
- 使用最少绝对收缩和选择运算符 (LASSO) 进行关键预测器识别.
- 开发并比较各种机器学习 (ML) 分类模型.
- 通过校准曲线,决策曲线分析和Shapley添加式扩展 (SHAP) 来验证模型性能,以获得可解释性.
主要成果:
- 极端梯度增强 (XGBoost) 模型在测试组中表现出最佳性能,曲线下面积 (AUC) 为0.774.
- 校准曲线证实了模型的准确性,决策曲线分析支持了它的临床实用性.
- SHAP分析提供了个性化的风险评估,提高了临床解释性.
结论:
- 开发的基于ML的预测模型是识别患有CD高风险的SLE患者的宝贵工具.
- 早期识别有助于及时进行预防性干预,可能减轻CD的进展.
- 该模型增强了针对个性化的患者管理的临床决策.
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