MSRPPnet: an automatic ICD coding method for clinical records 
based on a deep neural network

Xianfeng Liu; Yunzhu Zhang; Jin Zhang

doi:10.1117/12.2668317

20 April 2023 MSRPPnet: an automatic ICD coding method for clinical records based on a deep neural network

Xianfeng Liu, Yunzhu Zhang, Jin Zhang

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126023B (2023) https://doi.org/10.1117/12.2668317
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

The International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. In this work, we proposed an automatic ICD coding method for clinical records based on a co-occurrence disease perception network. It uses a multi-scale residual network to capture text patterns of different lengths of clinical text and Personalized PageRank to capture co-occurrence between codes to enhance the ability of automatic coding (MSRPPnet). As shown in the experimental results on the real medical dataset MIMIC-III, the P@N and Micro-F1 of this method are 72.2% and 55.1%, respectively, which significantly improves the prediction performances.

Citation Download Citation

Xianfeng Liu, Yunzhu Zhang, and Jin Zhang "MSRPPnet: an automatic ICD coding method for clinical records based on a deep neural network", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126023B (20 April 2023); https://doi.org/10.1117/12.2668317

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