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使用多个机器学习算法对狼性炎的共感染进行预测建模

Jiaqian Zhang¹, Bo Chen¹, Jiu Liu²

¹Department of Rheumatology and Immunology, West China Hospital, Sichuan University, No. 37 Guo Xue Lane, Wuhou District, Chengdu, 610041, Sichuan, China.

Scientific reports

|April 22, 2024

概括

患有感染的狼性炎 (LN) 患者的外周血液淋巴细胞子集减少. 机器学习,特别是XGBoost,通过分析这些免疫细胞水平,准确地预测LN患者的共感染.

科学领域:

免疫学免疫学免疫学
医疗信息学医疗信息学
腎臟病學 (nephrology) 是一種醫學專業.

背景情况:

狼性炎 (LN) 患者表现出改变的免疫特征,增加对感染的易感性.
了解周围血液淋巴细胞子集的变化对于管理LN并发症至关重要.

研究的目的:

在狼性炎 (LN) 患者中分析周围血液淋巴细胞子集.
开发一种机器学习 (ML) 算法,用于预测LN患者的共感染.

主要方法:

在111名未感染的LN,72名感染的LN患者和206名健康的对照人群 (HCs) 中比较了淋巴细胞子集 (T,B,辅助T,抑制T,NK,Tregs).
评估了八个ML算法 (物流回归,决策树,KNN,SVM,MLP,随机森林,Ada提升,XGBoost) 进行预测准确性.
在单独的测试组中验证了表现最好的ML模型.

主要成果:

与未感染LN患者和HCs相比,感染LN患者的T,B,辅助T,抑制T和自然杀手细胞水平显著降低.
调节性T细胞 (Tregs) 在LN患者中明显低于HC患者,感染LN患者的数量最低.
极端梯度提升 (XGBoost) 在预测LN联合感染方面取得了最高的准确性和精度.

结论:

在LN患者中,先天性和适应性免疫的破坏是显而易见的.
淋巴细胞子集的监测有助于预防和治疗LN感染.
XGBoost算法是一个有前途的工具,用于预测狼性炎的共感染.

关键词:

感染感染感染狼性炎是一种神经炎.淋巴细胞亚种群淋巴细胞亚种群机器学习是机器学习.

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