COPD-MMDDxNet:使用电子病历进行准确的COPD诊断的多式联络深度学习框架
Yuanyuan Yi1, Lei Shi2, Haoran Liu3
1Department of Respiratory Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Frontiers in medicine
|July 28, 2025
概括
一个新的深度学习模型,COPD-MMDDxNet,使用电子健康记录为慢性阻塞性肺病 (COPD) 提供准确的诊断,绕过了资源有限的环境中对螺旋计的需求.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 肺部病理学 肺部病理学
背景情况:
- 慢性阻塞性肺病 (COPD) 影响全球超过3.91亿人.
- 螺旋计,COPD诊断的GOLD标准,可用性有限,需要患者的合作,在初级保健和资源有限的环境中提出挑战.
- 迫切需要替代性,可访问的COPD诊断方法.
研究的目的:
- 开发和验证一种新的多式联络深度学习框架,COPD-MMDDxNet,用于在不使用螺旋计时的情况下诊断COPD.
- 整合结构化肺CT报告,血液气体分析和来自电子病历 (EMR) 的血液学数据.
- 创建第一个自旋计独立的COPD多式诊断工具.
主要方法:
- 开发了一种多式深度学习框架 (COPD-MMDDxNet),集成参数数值嵌入,层次交互机制,对比规范化和动态融合系数.
- 该框架的训练和评估是基于800名COPD患者的数据集,这些患者具有平衡的人口统计数据,收集了24个月的时间.
- 性能与单模式模型和其他最先进的多模式方法进行了比较.
主要成果:
- COPD-MMDDxNet实现了高的诊断性能:准确度 (81.76%),精度 (78.87%),回忆 (77.78%) 和F1得分 (78.32%).
- 废除研究证实了个体组件,特别是对比学习和跨模态注意力在提高诊断准确度方面的重要性.
- 该模型与传统的单模和现有的多模诊断方法相比,表现优越.
结论:
- COPD-MMDDxNet为COPD诊断提供了一个强大的,准确的和可访问的解决方案,特别是在资源有限的环境中.
- 这种不依赖于螺旋计的方法克服了传统诊断方法的局限性.
- 多式联络深度学习框架显示出改善全球COPD管理的希望.
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