BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis

Author:

Dai ZhihaoORCID,Li Zhong,Han LianghaoORCID

Publisher

Springer International Publishing

Reference26 articles.

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2. Bozkurt, S., Alkim, E., Banerjee, I., Rubin, D.L.: Automated detection of measurements and their descriptors in radiology reports using a hybrid natural language processing algorithm. Journal of Digital Imaging 32(4), 544–553 (2019). https://doi.org/10.1007/s10278-019-00237-9

3. Chambers, N., et al.: Learning alignments and leveraging natural logic. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing - RTE 2007. Association for Computational Linguistics, Morristown, NJ, USA, pp. 165–170 (2007). https://doi.org/10.3115/1654536.1654570

4. Charniak, E., Johnson, M.: Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics - ACL 2005. Association for Computational Linguistics, Morristown, NJ, USA, vol. 1, pp. 173–180 (2005). https://doi.org/10.3115/1219840.1219862

5. Datta, S., Si, Y., Rodriguez, L., Shooshan, S.E., Demner-Fushman, D., Roberts, K.: Understanding spatial language in radiology: representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning. J. Biomed. Inform. 108, 103473 (2019). https://doi.org/10.1016/j.jbi.2020.103473, http://arxiv.org/abs/1908.04485

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