TextC/R/RCNN for multi-label classification based ICD coding

Xu Han

doi:10.1117/12.2640708

13 October 2022 TextC/R/RCNN for multi-label classification based ICD coding

Xu Han

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870F (2022) https://doi.org/10.1117/12.2640708
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

The International Classification of Diseases (ICD) is a health care Classification system initiated by the World Health Organization. ICD codes are used to quantify important statistical data and facilitate the search for patient cohort with similar diagnosis. In addition, they are also of great value and significance as a means of standardized information exchange between hospitals. Manual ICD coding is a time-consuming and laborious work, now most people use machine/deep learning methods for automatic coding. TextCNN, TextRNN and TextRCNN have been the mainstream models of multilabel text classification task since they were proposed. In this paper, three language models are combined with ICD automatic coding task. The experimental results show that the three models have achieved good results. In addition to that, this paper demonstrates the limiting factors of the model performance through detailed experiments to guide the future work to make further progress.

Citation Download Citation

Xu Han "TextC/R/RCNN for multi-label classification based ICD coding", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870F (13 October 2022); https://doi.org/10.1117/12.2640708

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