A Deep Learning Method for ICD-10 Coding of Free-Text Death Certificates

  • Francisco Duarte
  • Bruno Martins
  • Cátia Sousa Pinto
  • Mário J. Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)

Abstract

The assignment of disease codes to clinical texts has a wide range of applications, including epidemiological studies or disease surveillance. We address the task of automatically assigning the ICD-10 codes for the underlying cause of death, from the free-text descriptions included in death certificates obtained from the Portuguese Ministry of Health. We specifically propose to leverage a deep neural network based on a two-level hierarchy of recurrent nodes together with attention mechanisms. The first level uses recurrent nodes for modeling the sequences of words given in individual fields of the death certificates, together with attention to weight the contribution of each word, producing intermediate representations for the contents of each field. The second level uses recurrent nodes to model a sequence of fields, using the representations produced by the first level and also leveraging attention in order to weight the contributions of the different fields. The paper reports on experiments with a dataset of 115,406 death certificates, presenting the results of an evaluation of the predictive accuracy of the proposed method, for different ICD-10 levels (i.e., chapter, block, or full code) and for particular causes of death. We also discuss how the neural attention mechanisms can help in interpreting the classification results.

Keywords

Classification of death certificates Clinical text mining Deep learning Natural language processing Artificial intelligence in medicine 

Notes

Acknowledgements

This work had support from Fundação para a Ciência e Tecnologia (FCT), through the INESC-ID multi-annual funding from the PIDDAC program (UID/CEC/50021/2013).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Francisco Duarte
    • 1
  • Bruno Martins
    • 1
  • Cátia Sousa Pinto
    • 2
  • Mário J. Silva
    • 1
  1. 1.INESC-ID, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal
  2. 2.Direção-Geral da SaúdeLisbonPortugal

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