ICD-10 auto-coding system using deep learning
Journal
WCSE 2020: 2020 10th International Workshop on Computer Science and Engineering
Pages
557-562
Date Issued
2020
Author(s)
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. © WCSE 2020.
Subjects
Deep learning; Deep Neural Network; ICD-10; Natural Language Processing (NLP)
SDGs
Other Subjects
Health insurance; Natural language processing systems; Signal encoding; Auto-coding; Coding system; F1 scores; Label predictions; Medical classification; Nlp techniques; Rule based; Deep learning
Type
conference paper