Title | Automatic recognition of abdominal lymph nodes from clinical text |
Publication Type | Conference Paper |
Year of Publication | 2020 |
Authors | Peng Y, Lee S, Elton DC, Shen T, Tang Y-xing, Chen Q, Wang S, Zhu Y, Summers R, Lu Z |
Conference Name | Proceedings of the 3rd Clinical Natural Language Processing Workshop |
Date Published | 11/2020 |
Publisher | Association for Computational Linguistics |
Abstract | 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 reports. 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. |
URL | https://www.aclweb.org/anthology/2020.clinicalnlp-1.12 |