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Transformers for Multi-label Classification of Medical Text: An Empirical Comparison

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12721)

Abstract

Recent advancements in machine learning-based multi-label medical text classification techniques have been used to help enhance healthcare and aid better patient care. This research is motivated by transformers’ success in natural language processing tasks, and the opportunity to further improve performance for medical-domain specific tasks by exploiting models pre-trained on health data. We consider transfer learning involving fine-tuning of pre-trained models for predicting medical codes, formulated as a multi-label problem. We find that domain-specific transformers outperform state-of-the-art results for multi-label problems with the number of labels ranging from 18 to 158, for a fixed sequence length. Additionally, we find that, for longer documents and/or number of labels greater than 300, traditional neural networks still have an edge over transformers. These findings are obtained by performing extensive experiments on the semi-structured eICU data and the free-form MIMIC III data, and applying various transformers including BERT, RoBERTa, and Longformer variations. The electronic health record data used in this research exhibits a high level of label imbalance. Considering individual label accuracy, we find that for eICU data medical-domain specific RoBERTa models achieve improvements for more frequent labels. For infrequent labels, in both datasets, traditional neural networks still perform better.

Keywords

Multi-label Fine-tuning Medical text Transformers Neural networks 

References

  1. 1.
    Alsentzer, E., et al.: Publicly available clinical BERT embeddings. In: Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72–78 (2019)Google Scholar
  2. 2.
    Amin, S., Neumann, G., Dunfield, K., Vechkaeva, A., Chapman, K.A., Wixted, M.K.: MLT-DFKI at CLEF eHealth 2019: multi-label classification of ICD-10 Codes with BERT. In: CLEF (Working Notes) (2019)Google Scholar
  3. 3.
    Amin-Nejad, A., Ive, J., Velupillai, S.: Exploring transformer text generation for medical dataset augmentation. In: Proceedings of The 12th Language Resources and Evaluation Conference, pp. 4699–4708 (2020)Google Scholar
  4. 4.
    Beltagy, I., Peters, M., Cohan, A.: Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150 (2020)
  5. 5.
    Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8), 2014 (2014)Google Scholar
  6. 6.
    Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-XL: attentive language models beyond a fixed-length context. In: ACL (2019)Google Scholar
  7. 7.
    Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (2019)Google Scholar
  8. 8.
    Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)CrossRefGoogle Scholar
  9. 9.
    Gu, Y., et al.: Domain-specific language model pretraining for biomedical natural language processing. arXiv preprint arXiv:2007.15779 (2020)
  10. 10.
    Gururangan, S., et al.: Don’t stop pretraining: adapt language models to domains and tasks. In: Proceedings of ACL (2020)Google Scholar
  11. 11.
    Johnson, A.E., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)CrossRefGoogle Scholar
  12. 12.
    Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics (2014)Google Scholar
  13. 13.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)Google Scholar
  14. 14.
    Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
  15. 15.
    Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., Joulin, A.: Advances in pre-training distributed word representations. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)Google Scholar
  16. 16.
    Moons, E., Khanna, A., Akkasi, A., Moens, M.F.: A comparison of deep learning methods for ICD coding of clinical records. Appl. Sci. 10(15), 5262 (2020)CrossRefGoogle Scholar
  17. 17.
    Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J., Eisenstein, J.: Explainable prediction of medical codes from clinical text. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1. ACL: New Orleans, LA, USA (2018)Google Scholar
  18. 18.
    Pollard, T.J., Johnson, A.E.W., Raffa, J.D., Celi, L.A., Mark, R.G., Badawi, O.: The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci. Data 5, 180178 (2018)CrossRefGoogle Scholar
  19. 19.
    Sänger, M., Weber, L., Kittner, M., Leser, U.: Classifying german animal experiment summaries with multi-lingual BERT at CLEF eHealth 2019 Task 1. In: CLEF (Working Notes) (2019)Google Scholar
  20. 20.
    Schäfer, H., Friedrich, C.: Multilingual ICD-10 code assignment with transformer architectures using MIMIC-III discharge summaries. In: CLEF 2020 (2020)Google Scholar
  21. 21.
    Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30, pp. 5998–6008 (2017)Google Scholar
  22. 22.
    Yogarajan, V., Gouk, H., Smith, T., Mayo, M., Pfahringer, B.: Comparing high dimensional word embeddings trained on medical text to bag-of-words for predicting medical codes. In: Nguyen, N.T., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds.) ACIIDS 2020. LNCS (LNAI), vol. 12033, pp. 97–108. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-41964-6_9CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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