使用机器学习模型预测药物耐药性患者对神经调节疗法的反应:元分析和系统审查
Alejandro Quintero-Villegas1,2,3,4, Fylaktis Fylaktou1,3,4,5, Jaclyn Morales1
1Northwell Health, New Hyde Park, NY, 11042, USA.
Bioelectronic medicine
|November 29, 2025
概括
机器学习模型显示,在接受神经调节的耐药患者中,预测治疗反应具有前途. 需要进一步的大规模研究来证实这些发现,并提高模型的通用性.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 耐药性影响着许多患者,需要先进的治疗策略.
- 神经调节疗法为治疗耐火性提供了一个替代方案.
- 预测治疗反应对于优化患者护理至关重要.
研究的目的:
- 系统地审查和分析关于机器学习 (ML) 的文献,以预测耐药性的神经调节反应.
- 评估当前ML模型在这个领域的性能和通用性.
主要方法:
- 在PubMed,Embase,Scopus和Cochrane数据库中进行系统的文献搜索.
- 包括使用ML模型预测神经调节反应的研究.
- 使用PROBAST进行质量评估和接受器操作特征曲线 (AUROC) 下面面积的元分析.
主要成果:
- 12项涉及535名患者的研究从最初确定的4,451名患者中被纳入.
- 神经刺激 (VNS) 是研究最多的神经调节 (11项研究).
- 聚合的AUROC为0.84,表明有希望的预测性能,支持矢量机器是最常见的ML模型.
结论:
- 多模式ML方法在预测药物耐药性的神经调节反应方面显示出潜力.
- 有限的外部验证和小型队列大小需要更大的前性研究.
- 基于ML的预测模型的提高通用性对于临床应用至关重要.
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