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Authors: Ayoub Bagheri 1 ; Arjan Sammani 2 ; Peter Van Der Heijden 3 ; Folkert Asselbergs 4 and Daniel Oberski 5

Affiliations: 1 Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, The Netherlands, Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands ; 2 Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands ; 3 Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, The Netherlands, S3RI, Faculty of Social Sciences, University of Southampton, U.K. ; 4 Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht, The Netherlands, Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, U.K., Health Data Research UK, Institute of Health Informatics, University College London, London, U.K. ; 5 Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, The Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

ISBN: 978-989-758-398-8

ISSN: 2184-4305

Keyword(s): Automated ICD Coding, Multi-label Classification, Clinical Text Mining, Dutch Discharge Letters.

Abstract: The international classification of diseases (ICD) is a widely used tool to describe patient diagnoses. At University Medical Center Utrecht (UMCU), for example, trained medical coders translate information from hospital discharge letters into ICD-10 codes for research and national disease epidemiology statistics, at considerable cost. To mitigate these costs, automatic ICD coding from discharge letters would be useful. However, this task has proven challenging in practice: it is a multi-label task with a large number of very sparse categories, presented in a hierarchical structure. Moreover, existing ICD systems have been benchmarked only on relatively easier versions of this task, such as single-label performance and performance on the higher “chapter” level of the ICD hierarchy, which contains fewer categories. In this study, we benchmark the state-of-the-art ICD classification systems and two baseline systems on a large dataset constructed from Dutch cardiology discharge letters a t UMCU hospital. Performance of all systems is evaluated for both the easier chapter-level ICD codes and single-label version of the task found in the literature, as well as for the lower-level ICD hierarchy and multi-label task that is needed in practice. We find that state-of-the-art methods outperform the baseline for the single-label version of the task only. For the multi-label task, the baselines are not defeated by any state-of-the-art system, with the exception of HA-GRU, which does perform best in the most difficult task on accuracy. We conclude that practical performance may have been somewhat overstated in the literature, although deep learning techniques are sufficiently good to complement, though not replace, human ICD coding in our application. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Bagheri, A.; Sammani, A.; Van Der Heijden, P.; Asselbergs, F. and Oberski, D. (2020). Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C, ISBN 978-989-758-398-8 ISSN 2184-4305, pages 281-289. DOI: 10.5220/0009372602810289

@conference{c2c20,
author={Ayoub Bagheri. and Arjan Sammani. and Peter {Van Der Heijden}. and Folkert Asselbergs. and Daniel Oberski.},
title={Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C,},
year={2020},
pages={281-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009372602810289},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C,
TI - Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters
SN - 978-989-758-398-8
IS - 2184-4305
AU - Bagheri, A.
AU - Sammani, A.
AU - Van Der Heijden, P.
AU - Asselbergs, F.
AU - Oberski, D.
PY - 2020
SP - 281
EP - 289
DO - 10.5220/0009372602810289

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