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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 11

Icd coding classification based on data augmentation and dilated convolution

Yan Jing1
Zhao Di1,2,3
Meng Jiana1
Lin Hongfei2
1. School of Computer Science & Engineering, Dalian Minzu University, Dalian Liaoning 116600, China
2. School of Computer Science & Technology, Dalian University of Technology, Dalian Liaoning 116024, China
3. Dalian Yongjia Electronic Technology Corporation, Dalian Liaoning 116024, China
DOI: 10.19734/j.issn.1001-3695.2024.03.0088

Abstract

To address the problems of unbalanced label distribution, excessively long medical record text and large label space in the International Classification of Diseases (ICD) coding classification task, this paper proposed an ICD coding classification method based on data augmentation and dilated convolution. Firstly, this method introduced the pre-trained model BioLinkBERT, trained in the biomedical domain using unsupervised learning, to alleviate the domain mismatch problem. Secondly, it applied the Mixup data augmentation technique to expand the hidden representations, thereby increasing data diversity and improving model robustness for classification, addressing the problem of imbalanced label distribution. Finally, the model effectively captured long-range dependencies in the text data using multi-granularity dilated convolution, avoiding the impact of long input text on the model's performance. The experimental results demonstrate that our model has achieved a notable improvement over the baseline model on two subsets of the MIMIC-III dataset when compared with various methods. Specifically, the F1 scores and Precision@k values improves 0.4% to 1.5% and 1.2% to 1.6%, respectively. Therefore, this study provides an effective solution to solve the challenges of ICD coding classification.

Foundation Support

辽宁省自然科学基金资助项目(2022-BS-104)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.03.0088
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 11

Publish History

[2024-08-01] Accepted Paper

Cite This Article

闫婧, 赵迪, 孟佳娜, 等. 基于数据增强和扩张卷积的ICD编码分类 [J]. 计算机应用研究, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.03.0088. (Yan Jing, Zhao Di, Meng Jiana, et al. Icd coding classification based on data augmentation and dilated convolution [J]. Application Research of Computers, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.03.0088. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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