Abstract
Although deep learning techniques have obtained remarkable results in clinical text analysis, the delicacy of this application domain requires also that these models can be easily understood by the hospital staff. The attention mechanism, which assigns numerical weights representing the contribution of each word to the predictive task, can be exploited for identifying the textual evidence the prediction is based on. In this paper, we investigate the explainability of an attention-based classification model for radiology reports collected from an Italian hospital. The identified explanations are compared with a set of manual annotations made by the domain experts in order to analyze the usefulness of the attention mechanism in our context.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd International Conference on Learning Representations (2015)
Gerevini, A.E., et al.: Automatic classification of radiological reports for clinical care. Artif. Intell. Med. 91, 72–81 (2018)
Jacovi, A., Goldberg, Y.: Towards faithfully interpretable NLP systems: how should we define and evaluate faithfulness? In: Proceedings of the 58th Annual Meeting of the ACL, pp. 4198–4205 (2020)
Jain, S., Wallace, B.C.: Attention is not explanation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 3543–3556 (2019)
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 ACL, pp. 1101–1111 (2018)
Putelli, L., Gerevini, A.E., Lavelli, A., Olivato, M., Serina, I.: Deep learning for classification of radiology reports with a hierarchical schema. In: Proceedings of the 24th International Conference KES-2020. Procedia Computer Science, vol. 176. Elsevier (2020)
Serrano, S., Smith, N.A.: Is attention interpretable? In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2931–2951 (2019)
Vashishth, S., Upadhyay, S., Tomar, G.S., Faruqui, M.: Attention interpretability across NLP tasks. CoRR arXiv:1909.11218 (2019)
Wiegreffe, S., Pinter, Y.: Attention is not not explanation. In: Proceedings of EMNLP-IJCNLP, pp. 11–20 (2019)
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Putelli, L., Gerevini, A.E., Lavelli, A., Maroldi, R., Serina, I. (2021). Attention-Based Explanation in a Deep Learning Model For Classifying Radiology Reports. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_42
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DOI: https://doi.org/10.1007/978-3-030-77211-6_42
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