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


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.
Anthology ID:
2020.clinicalnlp-1.12
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
101–110
Language:
URL:
https://aclanthology.org/2020.clinicalnlp-1.12
DOI:
10.18653/v1/2020.clinicalnlp-1.12
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.clinicalnlp-1.12.pdf
Video:
 https://slideslive.com/38939811