在类风湿性关节炎中预测治疗对生物和向合成疾病修饰性抗风湿药物的反应的机器学习:一个范围审查
Ehiremen Bennard Eriakha1, Yu Han2, Mai Li3
1Department of Pharmacy Administration, School of Pharmacy, University of Mississippi, Oxford, MS, USA.
BMC rheumatology
|November 11, 2025
概括
机器学习 (ML) 对预测类风湿性关节炎 (RA) 治疗对生物和向合成疾病修饰性抗风湿药物 (b/tsDMARDs) 的反应显示出希望. 为了临床使用,需要进一步的标准化和验证.
科学领域:
- 类风湿病学 类风湿病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 生物和向合成疾病修饰性抗风湿药物 (b/tsDMARDs) 改善了类风湿性关节炎 (RA) 的结果.
- 在RA中治疗反应的异质性仍然是一个重要的临床挑战.
- 机器学习 (ML) 提供了改善预测RA治疗反应的潜力.
研究的目的:
- 综合现有的关于ML方法的文献,用于预测RA中b/tsDMARDs的治疗反应.
- 在RA治疗反应预测中提供ML应用的全面概述.
- 确定ML方法论和RA报告中的差距和需要改进的领域.
主要方法:
- 在PubMed,MEDLINE和Embase的系统文献搜索 (2024年3月开始) 遵循PRISMA-ScR指南.
- 通过使用Covidence的两个独立审查员进行查和资格评估.
- 研究特征的定性综合,ML方法,预测因素,结果和验证;偏差风险 (QUIPS) 和报告质量 (TRIPOD) 评估.
主要成果:
- 24项研究符合纳入标准,主要使用注册表 (50%) 和电子健康记录 (17%) 数据.
- 提升树木,随机森林,SVM和规范回归是常见的ML模型.
- 预测性能各不相同 (AUC 0.540.92),增强树和神经网络往往表现出色;外部验证很少 (17.5%).
结论:
- ML方法越来越多地用于预测RA治疗反应.
- 在ML方法和报告的性能方面存在显著的变化.
- 标准化,强大的外部验证和透明的报告对于ML在RA治疗预测中的临床转化至关重要.
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