A Review on Deep Neural Networks for ICD Coding

The International Classification of Diseases (ICD) is a standard for categorizing physical conditions, which has been widely used for analyzing clinical data and monitoring health issues. Manual ICD coding takes a long time and is vulnerable to errors, so researchers pay more and more attention to the application of deep neural networks in ICD automatic coding. However, there is still no comprehensive review of these studies and prospects for further research. This paper is not limited to the study of deep neural networks, but gives a formal definition of ICD coding problems, and then systematically reviews the existing literature on how to design deep neural networks to address the four major challenges of ICD coding tasks. This paper also summarizes the public data sets and future research directions, to provide guidance for the research of ICD coding in medical field.

Automated summary

The International Classification of Diseases (ICD) is widely used for categorizing physical conditions. Manual ICD coding is time-consuming and prone to errors. Therefore, researchers are focusing on using deep neural networks for ICD automatic coding.