Daniel C. Elton
2020
Automatic recognition of abdominal lymph nodes from clinical text
Yifan Peng
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Sungwon Lee
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Daniel C. Elton
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Thomas Shen
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Yu-xing Tang
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Qingyu Chen
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Shuai Wang
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Yingying Zhu
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Ronald Summers
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Zhiyong Lu
Proceedings of the 3rd Clinical Natural Language Processing Workshop
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.
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Co-authors
- Yifan Peng 1
- Sungwon Lee 1
- Thomas Shen 1
- Yu-xing Tang 1
- Qingyu Chen 1
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