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Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning
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 la...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8441604/ https://www.ncbi.nlm.nih.gov/pubmed/34463639 http://dx.doi.org/10.2196/23230 |
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author | Chen, Pei-Fu Wang, Ssu-Ming Liao, Wei-Chih Kuo, Lu-Cheng Chen, Kuan-Chih Lin, Yu-Cheng Yang, Chi-Yu Chiu, Chi-Hao Chang, Shu-Chih Lai, Feipei |
author_facet | Chen, Pei-Fu Wang, Ssu-Ming Liao, Wei-Chih Kuo, Lu-Cheng Chen, Kuan-Chih Lin, Yu-Cheng Yang, Chi-Yu Chiu, Chi-Hao Chang, Shu-Chih Lai, Feipei |
author_sort | Chen, Pei-Fu |
collection | PubMed |
description | 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 F(1)-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 F(1)-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 F(1)-score significantly increased from a median of 0.832 to 0.922 (P<.05), but not in a reduced interval. CONCLUSIONS: The proposed model significantly improved the F(1)-score but did not decrease the time consumed in coding by disease coders. |
format | Online Article Text |
id | pubmed-8441604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-84416042021-09-28 Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning Chen, Pei-Fu Wang, Ssu-Ming Liao, Wei-Chih Kuo, Lu-Cheng Chen, Kuan-Chih Lin, Yu-Cheng Yang, Chi-Yu Chiu, Chi-Hao Chang, Shu-Chih Lai, Feipei JMIR Med Inform Original Paper 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 F(1)-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 F(1)-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 F(1)-score significantly increased from a median of 0.832 to 0.922 (P<.05), but not in a reduced interval. CONCLUSIONS: The proposed model significantly improved the F(1)-score but did not decrease the time consumed in coding by disease coders. JMIR Publications 2021-08-31 /pmc/articles/PMC8441604/ /pubmed/34463639 http://dx.doi.org/10.2196/23230 Text en ©Pei-Fu Chen, Ssu-Ming Wang, Wei-Chih Liao, Lu-Cheng Kuo, Kuan-Chih Chen, Yu-Cheng Lin, Chi-Yu Yang, Chi-Hao Chiu, Shu-Chih Chang, Feipei Lai. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 31.08.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Chen, Pei-Fu Wang, Ssu-Ming Liao, Wei-Chih Kuo, Lu-Cheng Chen, Kuan-Chih Lin, Yu-Cheng Yang, Chi-Yu Chiu, Chi-Hao Chang, Shu-Chih Lai, Feipei Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning |
title | Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning |
title_full | Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning |
title_fullStr | Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning |
title_full_unstemmed | Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning |
title_short | Automatic ICD-10 Coding and Training System: Deep Neural Network Based on Supervised Learning |
title_sort | automatic icd-10 coding and training system: deep neural network based on supervised learning |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8441604/ https://www.ncbi.nlm.nih.gov/pubmed/34463639 http://dx.doi.org/10.2196/23230 |
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