Automatic recognition of abdominal lymph nodes from clinical text

Yifan Peng, Sungwon Lee, Daniel C. Elton, Thomas Shen, Yu-xing Tang, Qingyu Chen, Shuai Wang, Yingying Zhu, Ronald Summers, Zhiyong Lu
2020 Proceedings of the 3rd Clinical Natural Language Processing Workshop   unpublished
Lymph node status plays a pivotal role in the treatment of cancer. The extraction of lymph nodes from radiology text reports enables large-scale training of lymph node detection on MRI. In this work, we first propose an ontology of 41 types of abdominal lymph nodes with a hierarchical relationship. We then introduce an end-to-end approach based on the combination of rules and transformer-based methods to detect these abdominal lymph node mentions and classify their types from the MRI radiology
more » ... eports. We demonstrate the superior performance of a model fine-tuned on MRI reports using BlueBERT, called MriBERT. We find that MriBERT outperforms the rule-based labeler (0.957 vs 0.644 in micro weighted F1-score) as well as other BERT-based variations (0.913 -0.928). We make the code and MriBERT publicly available at https://github.com/ ncbi-nlp/bluebert, with the hope that this method can facilitate the development of medical report annotators to produce labels from scratch at scale.
doi:10.18653/v1/2020.clinicalnlp-1.12 fatcat:4bgwn5epefc7tmwflfzqsdjwee