Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks

Abstract Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enor...

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Autores principales:	Arjan Sammani, Ayoub Bagheri, Peter G. M. van der Heijden, Anneline S. J. M. te Riele, Annette F. Baas, C. A. J. Oosters, Daniel Oberski, Folkert W. Asselbergs
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/0f7e2a6ca61e4c53a1d2909562865f85
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spelling	oai:doaj.org-article:0f7e2a6ca61e4c53a1d2909562865f852021-12-02T15:54:14ZAutomatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks10.1038/s41746-021-00404-92398-6352https://doaj.org/article/0f7e2a6ca61e4c53a1d2909562865f852021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00404-9https://doaj.org/toc/2398-6352Abstract Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enormously time-consuming endeavor. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic datasets, and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. We focussed on frequently used and well-defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21), or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5548 discharge letters and validated in 5089 discharge and procedural letters. As in clinical practice discharge letters may be labeled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy-sensitive information included in discharge letters, we added a de-identification step. The performance was high, with F1 scores of 0.76–0.99 for three-character and 0.87–0.98 for four-character ICD-10 codes, and was best when using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease the administrative burden of classifying discharge diagnoses and may serve as a scaffold for reimbursement and research applications.Arjan SammaniAyoub BagheriPeter G. M. van der HeijdenAnneline S. J. M. te RieleAnnette F. BaasC. A. J. OostersDaniel OberskiFolkert W. AsselbergsNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Arjan Sammani Ayoub Bagheri Peter G. M. van der Heijden Anneline S. J. M. te Riele Annette F. Baas C. A. J. Oosters Daniel Oberski Folkert W. Asselbergs Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
description	Abstract Standard reference terminology of diagnoses and risk factors is crucial for billing, epidemiological studies, and inter/intranational comparisons of diseases. The International Classification of Disease (ICD) is a standardized and widely used method, but the manual classification is an enormously time-consuming endeavor. Natural language processing together with machine learning allows automated structuring of diagnoses using ICD-10 codes, but the limited performance of machine learning models, the necessity of gigantic datasets, and poor reliability of terminal parts of these codes restricted clinical usability. We aimed to create a high performing pipeline for automated classification of reliable ICD-10 codes in the free medical text in cardiology. We focussed on frequently used and well-defined three- and four-digit ICD-10 codes that still have enough granularity to be clinically relevant such as atrial fibrillation (I48), acute myocardial infarction (I21), or dilated cardiomyopathy (I42.0). Our pipeline uses a deep neural network known as a Bidirectional Gated Recurrent Unit Neural Network and was trained and tested with 5548 discharge letters and validated in 5089 discharge and procedural letters. As in clinical practice discharge letters may be labeled with more than one code, we assessed the single- and multilabel performance of main diagnoses and cardiovascular risk factors. We investigated using both the entire body of text and only the summary paragraph, supplemented by age and sex. Given the privacy-sensitive information included in discharge letters, we added a de-identification step. The performance was high, with F1 scores of 0.76–0.99 for three-character and 0.87–0.98 for four-character ICD-10 codes, and was best when using complete discharge letters. Adding variables age/sex did not affect results. For model interpretability, word coefficients were provided and qualitative assessment of classification was manually performed. Because of its high performance, this pipeline can be useful to decrease the administrative burden of classifying discharge diagnoses and may serve as a scaffold for reimbursement and research applications.
format	article
author	Arjan Sammani Ayoub Bagheri Peter G. M. van der Heijden Anneline S. J. M. te Riele Annette F. Baas C. A. J. Oosters Daniel Oberski Folkert W. Asselbergs
author_facet	Arjan Sammani Ayoub Bagheri Peter G. M. van der Heijden Anneline S. J. M. te Riele Annette F. Baas C. A. J. Oosters Daniel Oberski Folkert W. Asselbergs
author_sort	Arjan Sammani
title	Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
title_short	Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
title_full	Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
title_fullStr	Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
title_full_unstemmed	Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks
title_sort	automatic multilabel detection of icd10 codes in dutch cardiology discharge letters using neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0f7e2a6ca61e4c53a1d2909562865f85
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Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks

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