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使用基于变压器的多模式深度学习预测化疗诱导的外周神经病变.

Sanghee Kim1

  • 1College of Nursing, Keimyung University, Daegu, Republic of Korea.

Research (Washington, D.C.)
|August 6, 2025
PubMed
概括

一种集成多式联络数据的新型深度学习模型准确预测化疗诱导的外围神经病变 (CIPN),改善患者护理,并使精确瘤学成为可能. 这种先进的预测可以帮助识别高风险患者进行有针对性的干预.

科学领域:

  • 在瘤学瘤学.
  • 计算生物学 计算生物学
  • 医疗信息学 医疗信息学

背景情况:

  • 化疗诱导的周围神经病变 (CIPN) 显著影响癌症患者的生活质量和治疗坚持.
  • 现有的CIPN使用单模数据的预测模型在临床应用中缺乏足够的准确性.

研究的目的:

  • 通过整合多式联络患者数据,开发和评估用于预测CIPN的深度学习模型.
  • 提高CIPN预测模型的临床实用性准确性和可解释性.

主要方法:

  • 基于变压器的深度学习架构用于中间数据融合.
  • 包括临床,基因组,生物信号,可穿戴设备和成像信息在内的多模式数据从电子健康记录和公共数据库中集成.
  • 使用SHAP和Grad-CAM实现了模型解释性,性能通过AUC-ROC,精度,灵敏度,特异性和F1得分进行评估.

主要成果:

  • 与传统模型相比,变压器模型实现了更高的性能 (AUC=0.93,精度=88.5%).
  • 确定的主要预测因素包括化疗剂量,神经核磁共振,心电图变化,CYP2C8突变和糖尿病.
  • 高预测的CIPN风险与总体存活率显著降低相关,这表明更广泛的系统影响.

结论:

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  • 集成多式联运数据的深度学习模型显著提高了CIPN预测的准确性.
  • 可解释的AI技术支持在精密瘤学中临床实施这些模型.
  • 未来的工作应该集中在多中心验证和为高危患者开发神经保护策略上.