An automatic ICD coding method for clinical records based on deep neural network

Yichao DU; Tong XU; Jianhui MA; Enhong CHEN; Yi ZHENG; Tongzhu LIU; Guixian TONG

doi:10.11959/j.issn.2096-0271.2020040

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An automatic ICD coding method for clinical records based on deep neural network

TOPIC：MEDICAL BIG DATA | 更新时间：2024-06-03

- An automatic ICD coding method for clinical records based on deep neural network
- Big Data Research Vol. 6, Issue 5, Pages: 2020040-1(2020)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2020040
  CLC： TP391
- Online First：2020-09，
  
  Published：15 September 2020
- 稿件说明：
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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：

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

SCHNAKERS C , VANHAUDENHUYSE A , GIACINO J , et al . Diagnostic accuracy of the vegetative and minimally conscious state:clinical consensus versus standardized neurobehavioral assessment [J ] . BMC Neurology , 2009 , 9 ( 1 ).

MULLENBACH J , WIEGREFFE S , DUKE J , et al . Explainable prediction of medical codes from clinical text [C ] // The 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.[S.l.:s.n] . 2018 .

JOHNSON A E W , POLLARD T J , SHEN L , et al . MIMIC-III:a freely accessible critical care database [J ] . Scientific Data , 2016 ( 3 ).

LARKEY L S , CROFT W B . Combining classifiers in text categorization [C ] // The 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 1996 : 289 - 297 .

FRANZ P , ZAISS A , SCHULZ S , et al . Automated coding of diagnoses-three methods compared [C ] // The AMIA Symposium.[S.l.:s.n] . 2000 .

PEROTTE A , PIVOVAROV R , NATARAJAN K , et al . Diagnosis code assignment:models and evaluation metrics [J ] . Journal of the American Medical Informatics Association , 2014 , 21 ( 2 ): 231 - 237 .

KAVULURU R , HAN S , HARRIS D . Unsupervised extraction of diagnosis codes from EMRs using knowledgebased and extractive text summarization techniques [C ] // 2013 Canadian Conference on Artificial Intelligence . Heidelberg:Springer-Verlag , 2013 : 77 - 88 .

KAVULURU R , RIOS A , LU Y . An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records [J ] . Artificial Intelligence in Medicine , 2015 , 65 ( 2 ): 155 - 166 .

KOOPMAN B , ZUCCON G , NGUYEN A , et al . Automatic ICD-10 classification of cancers from free-text death certificates [J ] . International Journal of Medical Informatics , 2015 , 84 ( 11 ): 956 - 965 .

SCHEURWEGS E , CULE B , LUYCKX K , et al . Selecting relevant features from the electronic health record for clinical code prediction [J ] . Journal of Biomedical Informatics , 2017 , 77 : 92 - 103 .

SHICKEL B , TIGHE P J , BIHORAC A , et al . Deep EHR:a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis [J ] . IEEE Journal of Biomedical and Health Informatics , 2017 , 22 ( 5 ): 1589 - 1604 .

LIPTON Z C , KALE D C , ELKAN C , et al . Learning to diagnose with LSTM recurrent neural networks [J ] . arXiv preprint , 2015 ,arXiv:1511.03677v7.

XU K , LAM M , PANG J , et al . Multimodal machine learning for automated ICD coding [J ] . arXiv preprint , 2018 ,arXiv:1810.13348v1.

SHI H , XIE P , HU Z , et al . Towards automated ICD coding using deep learning [J ] . arXiv preprint , 2017 ,arXiv:1711.04075v3.

XIE P , XING E . A neural architecture for automated ICD coding [C ] // The 56th Annual Meeting of the Association for Computational Linguistics.[S.l.:s.n] . 2018 : 1066 - 1077 .

DUARTE F , MARTINS B , PINTO C S , et al . Deep neural models for ICD-10 coding of death certificates and autopsy reports in free-text [J ] . Journal of Biomedical Informatics , 2018 , 80 : 64 - 77 .

PRAKASH A , ZHAO S , HASAN S A , et al . Condensed memory networks for clinical diagnostic inferencing [C ] // The 31st AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2017 : 3274 - 3280 .

BAUMEL T , NASSOUR-KASSIS J , COHEN R , , et al . Multi-label classification of patient notes:case study on ICD code assignment [C ] // The 32nd AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2018 .

ZENG M , LI M , FEI Z , et al . Automatic ICD-9 coding via deep transfer learning [J ] . Neurocomputing , 2019

CAI H , ZHENG V W , CHANG K C C . A comprehensive survey of graph embedding:problems,techniques,and applications [J ] . IEEE Transactions on Knowledge and Data Engineering , 2018 , 30 ( 9 ): 1616 - 1637 .

BATTAGLIA P W , HAMRICK J B , BAPST V , et al . Relational inductive biases,deep learning,and graph networks [J ] . arXiv preprint , 2018 ,arXiv:1806.01261v3.

BRUNA J , ZAREMBA W , SZLAM A , et al . Spectral networks and locally connected networks on graphs [J ] . arXiv preprint , 2013 ,arXiv:1312.6203v2.

DEFFERRARD M , BRESSON X , VANDERGHEYNST P . Convolutional neural networks on graphs with fast localized spectral filtering [C ] // Advances in Neural Information Processing Systems.[S.l.:s.n] . 2016 .

KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks [J ] . arXiv preprint , 2016 ,arXiv:1609.02907v4.

YAO L , MAO C , LUO Y . Graph convolutional networks for text classification [C ] // The 33rd AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2019 .

PENG H , LI J , HE Y , et al . Largescale hierarchical text classification with recursively regularized deep graphCNN [C ] // The 2018 World Wide Web Conference.[S.l.:s.n] . 2018 : 1063 - 1072 .

RIOS A , KAVULURU R . Few-shot and zero-shot multi-label learning for structured label spaces [C ] // The 2018 Conference on Empirical Methods in Natural Language Processing.[S.l.:s.n] . 2018 : 3132 - 3142 .

WANG W , XU H , GAN Z , et al . Graphdriven generative models for heterogeneous multi-task learning [C ] // The 34th AAAI Conference on Artificial Intelligence . PaloAlto:AAAI Press , 2020 .

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv preprint , 2013 ,arXiv:1301.3781v3.

KIM Y . Convolutional neural networks for sentence classification [J ] . arXiv preprint , 2014 ,arXiv:1408.5882v2.

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