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We investigate different natural language processing (NLP) approaches based on contextualised word representations for the problem of early prediction of lung cancer using free-text patient medical notes of Dutch primary care physicians.
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Moons, K.G.M., Wolff, R.F., Riley, R.D., Whiting, P.F., Westwood, M., Collins, G.S., Reitsma, J.B., Kleijnen, J., Mallett, S.: Probast: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration. Annals of internal medicine 170
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Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.: Superglue: A stickier benchmark for general-purpose language understanding systems. In: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019)
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Delobelle, P., Winters, T., Berendt, B.: RobBERT: a Dutch RoBERTa-based Language Model. In: Findings of the Association for Computational Linguistics: EMNLP 2020. pp. 3255–3265. Association for Computational Linguistics, Online (Nov 2020). https://doi.org/10.18653/v1/2020.findings-emnlp.292
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Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surv. 53
2020
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Luik, T.T., Rios, M., Abu-Hanna, A., van Weert, H.C.P.M., Schut, M.C.: The effectiveness of phrase skip-gram in primary care nlp for the prediction of lung cancer. In: Artificial Intelligence in Medicine. pp. 433–437. Springer Intern. Publishing (2021)
2021
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Rasmy, L., Xiang, Y., Xie, Z., Tao, C., Zhi, D.: Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. npj Digital Medicine 4
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Verkijk, S., Vossen, P.: Medroberta.nl: A language model for dutch electronic health records. Computational Linguistics in the Netherlands Journal 11
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Aken, B.v., Papaioannou, J.M., Mayrdorfer, M., Budde, K., Gers, F., Loeser, A.: Clinical outcome prediction from admission notes using self-supervised knowledge integration. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. pp. 881–893. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.eacl-main.75
2021
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Golmaei, S.N., Luo, X.: DeepNote-GNN. In: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. pp. 1–9. ACM (8 2021). https://doi.org/10.1145/3459930.3469547
2021
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Kim, S., Lee, C.k., Choi, Y., Baek, E.S., Choi, J.E., Lim, J.S., Kang, J., Shin, S.J.: Deep-learning-based natural language processing of serial free-text radiological reports for predicting rectal cancer patient survival. Frontiers in Oncology 11
2021
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Lester, B., Al-Rfou, R., Constant, N.: The power of scale for parameter-efficient prompt tuning. In: EMNLP 2021. pp. 3045–3059. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (Nov 2021). https://doi.org/10.18653/v1/2021.emnlp-main.243
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Li, X.L., Liang, P.: Prefix-tuning: Optimizing continuous prompts for generation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 4582–4597. Association for Computational Linguistics (2021). https://doi.org/10.18653/v1/2021.acl-long.353
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Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., Tang, J.: GPT understands, too. https://doi.org/10.48550/arXiv.2103.10385, version: 1
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2022
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Li, Y., Mamouei, M., Salimi-Khorshidi, G., Rao, S., Hassaine, A., Canoy, D., Lukasiewicz, T., Rahimi, K.: Hi-BEHRT: Hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records. IEEE journal of biomedical and health informatics (11 2022)
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Liu, X., Ji, K., Fu, Y., Tam, W., Du, Z., Yang, Z., Tang, J.: P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks. In: 60th ACL (Volume 2: Short Papers). pp. 61–68. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.acl-short.8
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Naseem, U., Dunn, A.G., Khushi, M., Kim, J.: Benchmarking for biomedical natural language processing tasks with a domain specific albert. BMC Bioinformatics 23
2022
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