Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning
| Autor: | Ssu Ming Wang, Kuan Chih Chen, Wei-Chih Liao, Chi Yu Yang, Feipei Lai, Yu Cheng Lin, Shu Chih Chang, Pei Fu Chen, Chi Hao Chiu, Lu-Cheng Kuo |
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| Rok vydání: | 2021 |
| Předmět: |
Original Paper
text classification Artificial neural network Computer science business.industry Deep learning Recurrent Neural Network Supervised learning deep learning Health Informatics Machine learning computer.software_genre Recurrent neural network Health Information Management International Classification of Diseases Artificial intelligence Language model natural language processing business Encoder computer Natural language Coding (social sciences) |
| Zdroj: | JMIR Medical Informatics |
| ISSN: | 2291-9694 |
| DOI: | 10.2196/23230 |
| Popis: | Background The 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. Objective This paper aims at constructing a deep learning model for ICD-10 coding, where the model is meant to automatically determine the corresponding diagnosis and procedure codes based solely on free-text medical notes to improve accuracy and reduce human effort. Methods We used diagnosis records of the National Taiwan University Hospital as resources and apply natural language processing techniques, including global vectors, word to vectors, embeddings from language models, bidirectional encoder representations from transformers, and single head attention recurrent neural network, on the deep neural network architecture to implement ICD-10 auto-coding. Besides, we introduced the attention mechanism into the classification model to extract the keywords from diagnoses and visualize the coding reference for training freshmen in ICD-10. Sixty discharge notes were randomly selected to examine the change in the F1-score and the coding time by coders before and after using our model. Results In experiments on the medical data set of National Taiwan University Hospital, our prediction results revealed F1-scores of 0.715 and 0.618 for the ICD-10 Clinical Modification code and Procedure Coding System code, respectively, with a bidirectional encoder representations from transformers embedding approach in the Gated Recurrent Unit classification model. The well-trained models were applied on the ICD-10 web service for coding and training to ICD-10 users. With this service, coders can code with the F1-score significantly increased from a median of 0.832 to 0.922 (P Conclusions The proposed model significantly improved the F1-score but did not decrease the time consumed in coding by disease coders. |
| Databáze: | OpenAIRE |
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