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基于机器学习的系统性红斑狼与高疾病活动的识别

Da-Cheng Wang¹, Wang-Dong Xu², Zhen Qin³

¹Department of Evidence-Based Medicine, Southwest Medical University, 1 Xianglin Road, Luzhou, 646000, Sichuan, China.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]

|September 19, 2023

概括

这项研究开发了一种机器学习模型,用于识别具有高疾病活性的全身性红斑狼 (SLE) 患者. 使用蛋白尿和血尿等特征的LGB模型,在识别SLE患者的高疾病活性方面取得了很高的准确性.

科学领域:

医疗信息学医学信息学
机器学习在医疗保健中的应用
类风湿病学类风湿病学

背景情况:

系统性红斑狼 (SLE) 疾病活动的临床评估具有挑战性和不一致性.
在SLE中高的疾病活性显著影响患者的结果.

研究的目的:

开发一种机器学习模型,用于识别具有高疾病活性的SLE患者.
提高SLE疾病活动评估的准确性和一致性.

主要方法:

利用了来自1014名低活性和453名高活性SLE患者的数据.
收集了94个临床,实验室和17个气象指标.
采用相互信息和多重冲浪来选择功能,然后采用机器学习建模 (LGB,Naive Bayes).

主要成果:

综合特征选择确定了关键指标:血液,蛋白尿,皮尿,低补,降水和阳光.
LGB模型表现出卓越的性能,ROC AUC为0.930,PRC AUC为0.911. 这两种模型的 ROC AUC均为0.930和PRC AUC均为0.911.
在复合特征重要性条图中确认了特征选择的一致性.

结论:

一个简单的机器学习管道可以有效地识别具有高疾病活性的SLE患者.
通过利用选定的特征,如蛋白尿和血液尿,LGB模型显示出临床应用的重大前景.

关键词:

疾病活动高的疾病活动.气象数据气象数据一个小小的东西.斯莱达伊 (SLEDAI) 是一个叫做"斯莱达伊"的公司.

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