Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning

Chen, Pei-FuPei-FuChenWang, Ssu-MingSsu-MingWangLiao, Wei-ChihWei-ChihLiaoLU-CHENG KUOChen, Kuan-ChihKuan-ChihChenLin, Yu-ChengYu-ChengLinYang, Chi-YuChi-YuYangChiu, Chi-HaoChi-HaoChiuChang, Shu-ChihShu-ChihChangFEI-PEI LAI2021-11-302021-11-302021-08-312291-9694https://scholars.lib.ntu.edu.tw/handle/123456789/588269The International Classification of Diseases (ICD) code is widely used as the reference in medical system and billing purposes. However, classifying diseases into ICD codes still mainly relies on humans reading a large amount of written material as the basis for coding. Coding is both laborious and time-consuming. Since the conversion of ICD-9 to ICD-10, the coding task became much more complicated, and deep learning- and natural language processing-related approaches have been studied to assist disease coders.enInternational Classification of Diseases; Recurrent Neural Network; deep learning; natural language processing; text classification[SDGs]SDG3[SDGs]SDG4Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learningjournal article10.2196/23230344636392-s2.0-85114289730https://scholars.lib.ntu.edu.tw/handle/123456789/583472