https://scholars.lib.ntu.edu.tw/handle/123456789/555702
標題: | ICD-10 auto-coding system using deep learning | 作者: | Wang, S.-M. FEI-PEI LAI Sung, C.-S. Chen, Y. |
關鍵字: | Deep learning; Deep Neural Network; ICD-10; Natural Language Processing (NLP) | 公開日期: | 2020 | 起(迄)頁: | 557-562 | 來源出版物: | WCSE 2020: 2020 10th International Workshop on Computer Science and Engineering | 摘要: | 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. |
URI: | https://www.scopus.com/inward/record.url?eid=2-s2.0-85092368088&partnerID=40&md5=f53cec8510344cbb8cd42df404130486 https://scholars.lib.ntu.edu.tw/handle/123456789/555702 |
DOI: | 10.18178/wcse.2020.02.008 | SDG/關鍵字: | Health insurance; Natural language processing systems; Signal encoding; Auto-coding; Coding system; F1 scores; Label predictions; Medical classification; Nlp techniques; Rule based; Deep learning |
顯示於: | 生醫電子與資訊學研究所 |
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