在对非类固醇抗炎药物过敏的个体中,机器学习模型的开发和验证
Rocío Sáenz de Santa María1,2,3, Rafael Núñez1,2, María Salas-Cassinello1,2
1Allergy Research Group, Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina-IBIMA Plataforma BIONAND, Malaga, Spain.
Journal of investigational allergology & clinical immunology
|February 9, 2026
概括
机器学习准确地识别了非类固醇抗炎药物 (NSAID) 过敏症患者,将他们与那些可以安全使用NSAIDs的人区分开来. 这改善了诊断,并降低了医疗保健成本.
科学领域:
- 过敏和免疫学 过敏和免疫学
- 计算医学是一种计算医学.
- 药物监督 药物监督 药物监督
背景情况:
- 药物过敏反应 (HSR) 经常是由非类固醇抗炎药物 (NSAID) 引发的.
- 准确诊断NSAID诱导的HSR是具有挑战性的,许多患者被错误识别.
- 错误诊断导致不必要的NSAID避免,影响患者护理和医疗资源.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于对NSAID过敏患者进行歧视.
- 为了区分真实的NSAID过敏症患者和那些疑似但未经证实的反应患者.
主要方法:
- 分析了疑似NSAID诱导的HSR患者的回顾性和前性队列.
- 开发和比较了多种ML模型,包括后勤回归和梯度增强机器.
- 最有效的模型在独立的潜在人群中得到了验证.
主要成果:
- 所有开发的模型都显示了很高的分类准确度 (≥85%).
- 轻度梯度增强机 (LGBM) 模型在初始测试中实现了99%的灵敏度和97%的准确性,实现了卓越的性能.
- 经过验证的LGBM模型显示准确率为91.76%,曲线下的面积为95.19%,在外部验证队列中准确率>95%.
结论:
- 开发的LGBM模型有效地区分了NSAID过敏患者和那些可以安全地容忍NSAID的患者.
- 在临床环境中实施可以提高NSAID诱导的HSR的诊断准确度.
- 这种ML方法可以帮助减少等待列表,并优化医疗保健资源分配.
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