END-TO-END RELATION EXTRACTION ON CLINICAL TEXT DATA USING NATURAL LANGUAGE PROCESSING
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Volume 4 (2), December 2021, Pages 232-241
Naveen S Pagad1, Pradeep N2
1 Visvesvaraya Technological University, Belagavi, Karnataka, India, firstname.lastname@example.org
In light of the increasing number of clinical narratives, a modern framework for assessing patient histories and carrying out clinical research has been developed. As a consequence of using existing approaches, the process for recognizing clinical entities and extracting relations from clinical narratives was subsequently error propagated. Thus, we propose an end-to-end clinical relation extraction model in this paper. Clinical XLNet has been used as the base model to address the discrepancy issue, and the proposed work has been tested with the N2C2 corpus.
Clinical entity, Relation extraction, Error propagation, End-to-end model.
Bethard, S., et al. (2015, June). Semeval-2015 task 6: Clinical tempeval. In proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) (pp. 806-814).
Bose, P., et al. (2021). A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts. Applied Sciences, 11(18), 8319.
Friedman, C., et al. (1994). A general natural-language text processor for clinical radiology. Journal of the American Medical Informatics Association, 1(2), 161-174.
Giorgi, J., et al. (2019). End-to-end named entity recognition and relation extraction using pre-trained language models. arXiv preprint arXiv:1912.13415.
Hasan, F., Roy, A., & Pan, S. (2020, November). Integrating Text Embedding with Traditional NLP Features for Clinical Relation Extraction. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 418-425). IEEE.
Jiang, S., Zhao, S., Hou, K., Liu, Y., & Zhang, L. (2019, October). A BERT-BiLSTM-CRF model for Chinese electronic medical records named entity recognition. In 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA) (pp. 166-169). IEEE.
Lee, H. J., et al. (2016, June). UTHealth at SemEval-2016 task 12: an end-to-end system for temporal information extraction from clinical notes. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (pp. 1292-1297).
Li, F., Zhang, M., Fu, G., & Ji, D. (2017). A neural joint model for entity and relation extraction from biomedical text. BMC bioinformatics, 18(1), 1-11.
Liao, W., & Veeramachaneni, S. (2009, June). A simple semi-supervised algorithm for named entity recognition. In Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing (pp. 58-65).
Mahendran, D., & McInnes, B. T. (2021). Extracting Adverse Drug Events from Clinical Notes. In AMIA Annual Symposium Proceedings (Vol. 2021, p. 420). American Medical Informatics Association.
Perera, N., Dehmer, M., & Emmert-Streib, F. (2020). Named entity recognition and relation detection for biomedical information extraction. Frontiers in cell and developmental biology, 673.
Shi, X., et al. (2019). Extracting entities with attributes in clinical text via joint deep learning. Journal of the American Medical Informatics Association, 26(12), 1584-1591.
Tang, B., Cao, H., Wu, Y., Jiang, M., & Xu, H. (2013, April). Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. In BMC medical informatics and decision making (Vol. 13, No. 1, pp. 1-10). BioMed Central.
Xu, J., He, H., Sun, X., Ren, X., & Li, S. (2018). Cross-domain and semisupervised named entity recognition in chinese social media: A unified model. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(11), 2142-2152.
Zhang, R., Zhao, P., Guo, W., Wang, R., & Lu, W. (2022). Medical named entity recognition based on dilated convolutional neural network. Cognitive Robotics, 2, 13-20.