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.
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Marques, C., Maia, C., Martins, H., Pinto, C.S., Anderson, R.N., Borralho, M.D.C.: Improving the mortality information system in portugal. Eurohealth 22(2), 48–51 (2016)
Dalianis, H.: Clinical text retrieval - an overview of basic building blocks and applications. In: Paltoglou, G., Loizides, F., Hansen, P. (eds.) Professional Search in the Modern World. LNCS, vol. 8830, pp. 147–165. Springer, Cham (2014). doi:10.1007/978-3-319-12511-4_8
Zweigenbaum, P., Lavergne, T.: Hybrid methods for ICD-10 coding of death certificates. In: Proceedings of International Workshop on Health Text Mining and Information Analysis (2016)
Mujtaba, G., Shuib, L., Raj, R.G., Rajandram, R., Shaikh, K., Al-Garadi, M.A.: Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection. PLoS ONE 12(2), e0170242 (2017)
Koopman, B., Zuccon, G., Nguyen, A., Bergheim, A., Grayson, N.: Automatic ICD-10 classification of cancers from free-text death certificates. Int. J. Med. Inform. 84(11), 956–965 (2015)
Koopman, B., Karimi, S., Nguyen, A., McGuire, R., Muscatello, D., Kemp, M., Truran, D., Zhang, M., Thackway, S.: Automatic classification of diseases from free-text death certificates for real-time surveillance. BMC Med. Inform. Decis. Making 15(1), 53 (2015)
Kelly, L., Goeuriot, L., Suominen, H., Névéol, A., Palotti, J., Zuccon, G.: Overview of the CLEF eHealth evaluation lab 2016. In: Fuhr, N., Quaresma, P., Gonçalves, T., Larsen, B., Balog, K., Macdonald, C., Cappellato, L., Ferro, N. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 255–266. Springer, Cham (2016). doi:10.1007/978-3-319-44564-9_24
Lavergne, T., Névéol, A., Robert, A., Grouin, C., Rey, G., Zweigenbaum, P.: A dataset for ICD-10 coding of death certificates: creation and usage. In: Proceedings of the Workshop on Building and Evaluating Resources for Biomedical Text Mining (2016)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (2016)
Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches (2014). arXiv preprint arXiv:1409.1259
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Cogn. model. 5(3), 1 (1988)
Kingma, D., Adam, J.B.: A method for stochastic optimization. In: Proceedings of the International Conference for Learning Representations (2015)
Goldberg, Y.: A primer on neural network models for natural language processing. J. Artif. Intell. Res. 57, 345–420 (2016)
Kurata, G., Xiang, B., Zhou, B.: Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In: Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (2016)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Philip, W., Kegelmeyer, S.: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification (2016). arXiv preprint arXiv:1607.01759
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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Duarte, F., Martins, B., Pinto, C.S., Silva, M.J. (2017). A Deep Learning Method for ICD-10 Coding of Free-Text Death Certificates. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_12
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