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Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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通过使用非结构化和结构化医疗保健数据,组建用于ICD代码预测的神经模型.

Alimurtaza Mustafa Merchant1, Naveen Shenoy1, Sidharth Lanka1

  • 1Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Srinivas Nagar P.O., Mangalore, 575025, Karnataka, India.

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概括

本研究介绍了一种AI模型,用于从临床笔记中自动编码疾病,提高医疗保健的准确性和效率. 组合模型将结构化和非结构化数据结合起来,在现实世界部署中显示出卓越的性能.

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人工智能的人工智能是人工智能.自动医疗编码自动化医学编码医疗保健信息学 医疗保健信息学标签注意力 标签注意力非结构化的文本建模.

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科学领域:

  • 医疗保健中的人工智能
  • 医疗信息学 医疗信息学
  • 临床自然语言处理 临床自然语言处理

背景情况:

  • 疾病编码对于患者跟踪至关重要,但手动,昂贵,容易出错.
  • 通过人工智能 (AI) 自动化疾病编码对于高效的医院信息管理系统至关重要.
  • 基于卷积神经网络 (CNN) 的方法目前代表了自动编码的最新技术.

研究的目的:

  • 提出一种新的神经模型,用于使用非结构化临床文本进行自动诊断编码.
  • 提高模型学习标签特定特征和相关临床文本片段的能力.
  • 通过整合代码描述和通过组合考虑结构化临床数据来改进诊断代码预测.

主要方法:

  • 开发了一个神经模型,利用非结构化的临床文本 (放电摘要).
  • 整合了一个结构化的自我注意机制,以识别标签特定的矢量和关键文本片段.
  • 集成了一个代码描述管道和探索模型,确保在结构化数据上进行监督机器学习 (随机森林,提升).

主要成果:

  • 拟议的模型在MIMIC-III数据集上实现了最先进的性能,超过了Longformer和知识图模型.
  • 组合模型将非结构化和结构化数据结合起来,比仅使用一个数据类型的模型表现出更高的性能.
  • 这些发现突出了整体方法在提高诊断代码预测准确性的潜力.

结论:

  • 开发的AI模型有效地自动化了从临床笔记中的诊断编码.
  • 集成模型整合了非结构化和结构化临床数据,为现实世界医疗保健应用提供了显著的优势.
  • 这种方法有望提高医院信息系统中疾病编码的准确性和效率.