A Hierarchical Approach for Extracting and Displaying Entities and Relations from Radiology Medical Reports

• Gabriel Toyoda UFAM
• Yunevda Rojas UFAM
• Juan G. Colonna UFAM
• Joao Gama Universidade do Porto

DOI: https://doi.org/10.5753/sbcas.2024.2089

Resumo

Extracting information from medical reports can be challenging due to the large volume of data. Therefore, this study proposes a method that uses a hierarchical classification approach with two levels, each consisting of a neural network instance. One for extracting clinical anatomical or observational entities along with their levels of uncertainty, and another for classifying the relations that exist between these entities. For this research, 600 radiological reports from the RadGraph dataset were used. The entity extraction task achieved an F1-score of 91%, while the entity classification and relation classification tasks achieved 88% each. Our hierarchical method enhances entity and relation classification performance by filtering and double checking classified entries.

Referências

A. Casey, E. Davidson, M. Poon, H. Dong, D. Duma, A. Grivas, C. Grover, V. Suárez-Paniagua, R. Tobin, W. Whiteley, et al. A systematic review of natural language processing applied to radiology reports. BMC medical informatics and decision making, 21(1):179, 2021.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.

H. M. Hapke, H. Lane, and C. Howard. Natural language processing in action: Understanding, analyzing, and generating text with python, 2023.

S. Jain, A. Agrawal, A. Saporta, S. Q. Truong, D. N. Duong, T. Bui, P. Chambon, Y. Zhang, M. P. Lungren, A. Y. Ng, et al. Radgraph: Extracting clinical entities and relations from radiology reports. arXiv preprint arXiv:2106.14463, 2021.

P. B. Jensen, L. J. Jensen, and S. Brunak. Mining electronic health records: towards better research applications and clinical care. Nature Reviews Genetics, 13(6):395–405, 2012.

M. Y. Landolsi, L. Hlaoua, and L. Ben Romdhane. Information extraction from electronic medical documents: state of the art and future research directions. Knowledge and Information Systems, 65(2):463–516, 2023.

C. Liu, W. Sun, W. Chao, and W. Che. Convolution neural network for relation extraction. In International conference on advanced data mining and applications, pages 231–242. Springer, 2013.

A. Naik and H. Rangwala. Introduction, pages 1–11. Springer International Publishing, Cham, 2018. ISBN 978-3-030-01620-3. DOI: 10.1007/978-3-030-01620-3, 1. URL DOI: 10.1007/978-3-030-01620-3_1.

E. Pons, L. M. Braun, M. M. Hunink, and J. A. Kors. Natural language processing in radiology: a systematic review. Radiology, 279(2):329–343, 2016.

C. N. d. Santos, B. Xiang, and B. Zhou. Classifying relations by ranking with convolutional neural networks. arXiv preprint arXiv:1504.06580, 2015.

O. Solarte-Pabón, O. Montenegro, A. Blazquez-Herranz, H. Saputro, A. Rodriguez-González, and E. Menasalvas. Information extraction from spanish radiology reports using multilingual bert. CLEF eHealth, 2021.

K. Sugimoto, T. Takeda, J.-H. Oh, S. Wada, S. Konishi, A. Yamahata, S. Manabe, N. Tomiyama, T. Matsunaga, K. Nakanishi, et al. Extracting clinical terms from radiology reports with deep learning. Journal of Biomedical Informatics, 116:103729, 2021.

L. Wang, Z. Cao, G. De Melo, and Z. Liu. Relation classification via multi-level attention cnns. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1298–1307, 2016.

S. Wu and Y. He. Enriching pre-trained language model with entity information for relation classification. In Proceedings of the 28th ACM international conference on information and knowledge management, pages 2361–2364, 2019.

W.-w. Yim, T. Denman, S. W. Kwan, and M. Yetisgen. Tumor information extraction in radiology reports for hepatocellular carcinoma patients. AMIA Summits on Translational Science Proceedings, 2016:455, 2016.

D. Zeng, K. Liu, S. Lai, G. Zhou, and J. Zhao. Relation classification via convolutional deep neural network. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers, pages 2335–2344, 2014.