Research Article
Using Deep Learning for Automatic Icd-10 Classification from Free-Text Data
Author(s):
Ssu-Ming Wang, Yu-Hsuan Chang, Lu-Cheng Kuo, Feipei Lai*, Yun-Nung Chen, Fei-Yun Yu, Chih-Wei Chen, Chung-Wei Lee and Yufang Chung
Background: Classifying diseases into ICD codes has mainly relied on human reading a large amount of written materials, such as discharge diagnoses, chief complaints, medical history, and operation records as the basis for classification. Coding is both laborious and time consuming because a disease coder with professional abilities takes about 20 minutes per case in average. Therefore, an automatic code classification system can significantly reduce the human effort. Objectives: This paper aims at constructing a machine learning model for ICD-10 coding, where the model is to automatically determine the corresponding diagnosis codes solely based on free-text medical notes. Methods: In this paper, we apply Natural Language Processing (NLP) and Recurrent Neural Network (RNN) architecture to classify ICD-10 codes from natural language texts with supervised le.. Read More»