一种基于深度神经网络的临床记录ICD自动编码方法

杜逸超; 徐童; 马建辉; 陈恩红; 郑毅; 刘同柱; 童贵显

doi:10.11959/j.issn.2096-0271.2020040

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一种基于深度神经网络的临床记录ICD自动编码方法

专题：医学大数据 | 更新时间：2024-06-03

- 一种基于深度神经网络的临床记录ICD自动编码方法
- An automatic ICD coding method for clinical records based on deep neural network
- 大数据 2020年6卷第5期页码：2020040-1
- 作者机构：
  
  1. 中国科学技术大学计算机科学与技术学院，安徽合肥 230027
  2. 华为技术有限公司，浙江杭州 310007
  3. 中国科学技术大学附属第一医院，安徽合肥 230027
- 作者简介：
  
  [ "杜逸超（1997- ），男，中国科学技术大学计算机科学与技术学院硕士生，主要研究方向为数据挖掘、知识图谱" ]
  [ "徐童（1988- ），男，博士，中国科学技术大学计算机科学与技术学院副教授，主要研究方向为数据挖掘" ]
  [ "马建辉（1975- ），男，中国科学技术大学计算机科学与技术学院讲师，主要研究方向为数据挖掘" ]
  [ "陈恩红（1968- ），男，博士，中国科学技术大学计算机科学与技术学院教授，主要研究方向为数据挖掘和机器学习" ]
  [ "郑毅（1987- ），男，博士，华为技术有限公司自然语言处理技术专家，主要研究方向为自然语言处理和机器学习" ]
  [ "刘同柱（1967- ），男，博士，中国科学技术大学附属第一医院副研究员，主要研究方向为健康大数据和医院管理" ]
  [ "童贵显（1991- ），男，中国科学技术大学附属第一医院初级经济师，主要研究方向为健康大数据和医院管理" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(U1605251);国家自然科学基金资助项目;The National Natural Science Foundation of China(61703386);中央高校基本科研业务费专项资金项目;The Fundamental Research Funds for the Central Universities(WK911000014);安徽省重点研发计划项目;The Key Research and Development Program of Anhui Province(1804b06020377)
- DOI：10.11959/j.issn.2096-0271.2020040
  中图分类号： TP391
- 网络首发：2020-09，
  
  纸质出版：2020-09-15
- 稿件说明：
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杜逸超, 徐童, 马建辉, 等. 一种基于深度神经网络的临床记录ICD自动编码方法[J]. 大数据, 2020,6(5):2020040-1.

Yichao DU, Tong XU, Jianhui MA, et al. An automatic ICD coding method for clinical records based on deep neural network[J]. Big Data Research, 2020, 6(5): 2020040-1.
杜逸超, 徐童, 马建辉, 等. 一种基于深度神经网络的临床记录ICD自动编码方法[J]. 大数据, 2020,6(5):2020040-1. DOI： 10.11959/j.issn.2096-0271.2020040.

Yichao DU, Tong XU, Jianhui MA, et al. An automatic ICD coding method for clinical records based on deep neural network[J]. Big Data Research, 2020, 6(5): 2020040-1. DOI： 10.11959/j.issn.2096-0271.2020040.

摘要

随着国际疾病分类（international classification of diseases，ICD）编码数量的增加，基于临床记录的人工编码难度和成本大大提高，自动ICD编码技术引起了广泛的关注。提出一种基于多尺度残差图卷积网络的自动ICD编码技术，该技术采用多尺度残差网络来捕获临床文本的不同长度的文本模式，并基于图卷积神经网络抽取标签之间的层次关系，以加强自动编码能力。在真实医疗数据集MIMIC-III上的实验结果表明，该方法的P@k和Micro-F1分别为72.2%和53.9%，显著提高了预测性能。

Abstract

With the increase in the number of the international classification of diseases (ICD) codes

the difficulty and cost of manual coding based on clinical records have greatly increased

and automatic ICD coding technology has attracted widespread attention.A multi-scale residual graph convolution network automatic ICD coding technology was proposed.This technology uses a multi-scale residual network to capture text patterns of different lengths of clinical text and extracts the hierarchical relationship between labels based on the graph convolutional neural network to enhance the ability of automatic coding.The experimental results on the real medical data set MIMIC-III show that the P@k and Micro-F1 of this method are 72.2% and 53.9%

respectively

which significantly improves the prediction performance.

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Keywords

references

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