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疾病国际自动分类编码通过注释代码交互网络与拒绝机制编码.

Xiaobo Li1, Yijia Zhang1, Xingwang Li1

  • 1School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China.

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概括

注释代码交互拒绝网络 (NIDN) 通过使用自我注意来提取电子医疗记录 (EMR) 中的关键信息并减少噪音来改进自动医疗编码. 这种深度学习方法可以提高国际疾病分类 (ICD) 代码的准确性.

关键词:
注意力机制注意力机制自动ICD编码自动化ICD编码消毒模块的消毒模块多任务学习学习

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能的人工智能
  • 临床文档 临床文档

背景情况:

  • 从临床笔记中准确编码医疗编码是具有挑战性的,因为数据的复杂性,大体积和噪音.
  • 深度学习对国际疾病分类 (ICD) 自动编码有希望,但面临着类不平衡和代码关联复杂性等问题.
  • 现有的方法与噪音记录和不平衡的代码分布作斗争,限制了电子医疗记录 (EMR) 分析的准确性.

研究的目的:

  • 开发一个先进的深度学习模型,用于准确的自动医学编码.
  • 解决ICD编码的挑战,包括数据噪声,类不平衡和复杂的代码关系.
  • 改进从临床笔记中提取语义特征和代码特定表达式.

主要方法:

  • 引入了使用自我注意机制从EMR中提取特征的Note-code交互拒绝网络 (NIDN).
  • 使用标签注意力机制来保留代码特定的文本信息.
  • 集成临床分类 软件编码用于多任务学习,并集成了一个消除噪音的模块来缓解噪音和失衡.

主要成果:

  • 与医疗信息中心重症监护 (MIMIC-III) 数据集中的现有模型相比,NIDN模型显示出更高的性能.
  • 自我注意和标签注意机制有效地捕获了关键的语义特征和代码特定的表达式.
  • 消噪模块成功地减少了噪声的影响,并改善了处理标签分配不平衡的问题.

结论:

  • NIDN模型代表了自动ICD编码的深度学习的重大进步.
  • 提出的方法有效地解决了处理复杂和杂的临床数据的关键挑战.
  • NIDN提供了一个强大的解决方案,以提高EMR医疗编码的准确性和效率.