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一个多中心可解释的机器学习分析在结性脊髓炎中自身免疫性疾病的并发症

Jichong Zhu¹, Chengqian Huang^2,3, Yang Lin⁴

¹The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Frontiers in immunology

|March 16, 2026

概括

机器学习使用常规数据准确地识别了结性脊柱炎 (AS) 的自身免疫并发症. 这有助于早期风险分层和AS患者和同时发生的自身免疫疾病的临床决策.

科学领域:

类风湿病学类风湿病学
人工智能的人工智能
临床信息学临床信息学

背景情况:

结性脊柱炎 (AS) 经常与其他自身免疫性疾病共存,使诊断复杂化.
在AS中早期识别这些并发症是一个临床挑战.

研究的目的:

开发和验证一种机器学习框架,用于识别AS的自身免疫性并发症.
描述AS与自身免疫共患病的独特临床和实验室特征.

主要方法:

一项多中心回顾性研究分析了2012-2025年的数据.
机器学习模型 (LightGBM) 被训练来对患者进行分类.
为了模型的可解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

轻GBM在识别与自身免疫共发病症的AS方面表现优异.
关键预测因素包括功能标志物,年龄,性别和肝功能指数.
在AS与自身免疫性并发症患者中观察到明显的代谢和炎症模式.

结论:

一个可解释的机器学习框架可以使用常规数据准确地识别AS的自身免疫共发症.
这种方法为早期风险分层和临床决策支持提供了潜力.

关键词:

轻GBMM 轻GBMM 的时间这就是 SHAP SHAP 的意思.结性脊柱炎是一种疾病.自体免疫性并发症机器学习是机器学习.

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