ICD-10 Auto-coding System Using Deep Learning
Abstract— In this research, we aim to construct an automatic ICD-10 coding system. ICD-10 is a medical classification standard which is strongly related to scope of payment in health insurance. However, the work of ICD-10 coding is time-consuming and tedious to ICD coders. Therefore, we build an ICD-10 coding system based on NLP approach to reduce their workload. The result of f1-score in whole label prediction task is up to 0.67 and 0.58 in CM and PCS, respectively. In addition, recall@20 in whole label prediction task is up to 0.87 and 0.81 in CM and PCS, respectively. In the future, we will keep working on combining the current work with the rule-based coding system and applying the other brand new NLP techniques to improve our performance.
Index Terms— Deep learning, Deep Neural Network, Natural Language Processing (NLP), ICD-10
Ssu-Ming Wang, Feipei Lai, Chang-Sung Sung, Yang Chen
National Taiwan Univ., TAIWAN
Cite: Ssu-Ming Wang, Feipei Lai, Chang-Sung Sung, Yang Chen, "ICD-10 Auto-coding System Using Deep Learning," Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering (WCSE 2020), pp. 46-51, Yangon (Rangoon), Myanmar (Burma), February 26-28, 2020.