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Deep Learning for ICD Coding: Looking for Medical Concepts in Clinical Documents in English and in French

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Book cover Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11018))

Abstract

Medical Concept Coding (MCD) is a crucial task in biomedical information extraction. Recent advances in neural network modeling have demonstrated its usefulness in the task of natural language processing. Modern framework of sequence-to-sequence learning that was initially used for recurrent neural networks has been shown to provide powerful solution to tasks such as Named Entity Recognition or Medical Concept Coding. We have addressed the identification of clinical concepts within the International Classification of Diseases version 10 (ICD-10) in two benchmark data sets of death certificates provided for the task 1 in the CLEF eHealth shared task 2017. A proposed architecture combines ideas from recurrent neural networks and traditional text retrieval term weighting schemes. We found that our models reach accuracy of 75% and 86% as evaluated by the F-measure on the CépiDc corpus of French texts and on the CDC corpus of English texts, respectfully. The proposed models can be employed for coding electronic medical records with ICD codes including diagnosis and procedure codes.

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Notes

  1. 1.

    https://github.com/dartrevan/clef_2017.

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Acknowledgements

This work was supported by the Russian Science Foundation grant no. 18-11-00284. The authors are grateful to Prof. Alexander Tropsha and Valentin Malykh for useful discussions about this study.

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Authors and Affiliations

  1. Kazan (Volga Region) Federal University, Kazan, Russia

    Zulfat Miftahutdinov & Elena Tutubalina

  2. Neuromation OU, 10111, Tallinn, Estonia

    Zulfat Miftahutdinov & Elena Tutubalina

Authors
  1. Zulfat Miftahutdinov
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  2. Elena Tutubalina
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Corresponding author

Correspondence to Elena Tutubalina .

Editor information

Editors and Affiliations

  1. Aix-Marseille University, Marseille Cedex 20, France

    Patrice Bellot

  2. Virtual University of Tunis, Tunis, Tunisia

    Chiraz Trabelsi

  3. Systèmes d’informations, Big Data et Rec, Institut de Recherche en Informatique de, Toulouse Cedex 04, France

    Josiane Mothe

  4. Department of Computer Science, University of Huddersfield, Huddersfield, United Kingdom

    Fionn Murtagh

  5. DIRO, Universite de Montreal, Montreal, Québec, Canada

    Jian Yun Nie

  6. Pierre and Marie Curie University, Paris Cedex 05, France

    Laure Soulier

  7. Université d'Avignon et des Pays de, Avignon, France

    Eric SanJuan

  8. Department of Information Engineering, University of Padua, Padua, Padova, Italy

    Linda Cappellato

  9. University of Padua, Padua, Italy

    Nicola Ferro

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Miftahutdinov, Z., Tutubalina, E. (2018). Deep Learning for ICD Coding: Looking for Medical Concepts in Clinical Documents in English and in French. In: Bellot, P., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2018. Lecture Notes in Computer Science(), vol 11018. Springer, Cham. https://doi.org/10.1007/978-3-319-98932-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-98932-7_19

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-98932-7

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