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通过基于Lambda-Scaled注意力的深度学习模型自动编码ICD-10-CM.

Sajida Raz Bhutto1, Min Zeng2, Kunying Niu2

  • 1School of Computer Science and Engineering, Central South University, Changsha 410083, PR China; Department of Software Engineering, MUET, SZAB (campus) Khairpur Mir's, Pakistan.

Methods (San Diego, Calif.)
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概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习模型,用于自动国际疾病分类 (ICD) -10-CM编码,克服注意力得分消失问题. DRCNN-ATT模型在肝移植和MIMIC III数据集上表现出卓越的性能,从而实现了高效的医疗数据管理.

关键词:
注意力机制注意力机制自动ICD编码自动化ICD编码深度学习技巧 深度学习技巧这就是Flask API.医学 临床笔记 医学 临床笔记

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 健康数据管理 管理健康数据

背景情况:

  • 国际疾病分类 (ICD) -10-CM是医疗管理的全球标准,对于准确的疾病分类至关重要.
  • 尽管广泛采用,但巴基斯坦的公共医院尚未实施自动化ICD-10-CM编码.
  • 医疗数据的复杂性需要先进的方法来准确的诊断分类.

研究的目的:

  • 开发和评估用于自动化ICD-10-CM编码的新型深度学习模型.
  • 解决注意力得分在现有的深度反复卷积神经网络 (DRCNN) 模型中消失的问题.
  • 实施和部署拟议的模型,以便在医疗保健环境中实际应用.

主要方法:

  • 一个私人肝移植临床池 (CPLT) 数据库被用于实施ICD-10-CM编码.
  • 提出了一个新的深度学习模型,深度反复卷积神经网络与兰巴尺度注意力模块 (DRCNN-ATT).
  • DRCNN-ATT模型整合了双向长期短期记忆 (bi-LSTM),多尺度卷积神经网络 (MS-CNN) 和一个兰巴达尺度的注意力模块.

主要成果:

  • 拟议的兰巴达尺度注意力模块有效地解决了在标准DRCNN模型中观察到的注意力得分消失问题.
  • DRCNN-ATT模型实现了高性能,在CPLT数据集上微F1得分为0.862,在MIMIC III前50名数据集上为0.705.
  • 该模型使用ngrok和Flask API成功部署,用于实时自动ICD-10-CM编码.

结论:

  • DRCNN-ATT模型代表了自动化ICD-10-CM编码的重大进步,提供了更高的准确性和效率.
  • 成功部署凸显了该模型在实际集成到医院信息系统中的潜力.
  • 这项研究有助于通过先进的人工智能技术实现医疗管理现代化.