@inproceedings{peng-etal-2020-automatic,
title = "Automatic recognition of abdominal lymph nodes from clinical text",
author = "Peng, Yifan and
Lee, Sungwon and
Elton, Daniel C. and
Shen, Thomas and
Tang, Yu-xing and
Chen, Qingyu and
Wang, Shuai and
Zhu, Yingying and
Summers, Ronald and
Lu, Zhiyong",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.clinicalnlp-1.12",
doi = "10.18653/v1/2020.clinicalnlp-1.12",
pages = "101--110",
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 \url{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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<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.</abstract>
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%0 Conference Proceedings
%T Automatic recognition of abdominal lymph nodes from clinical text
%A Peng, Yifan
%A Lee, Sungwon
%A Elton, Daniel C.
%A Shen, Thomas
%A Tang, Yu-xing
%A Chen, Qingyu
%A Wang, Shuai
%A Zhu, Yingying
%A Summers, Ronald
%A Lu, Zhiyong
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 3rd Clinical Natural Language Processing Workshop
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F peng-etal-2020-automatic
%X 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.
%R 10.18653/v1/2020.clinicalnlp-1.12
%U https://aclanthology.org/2020.clinicalnlp-1.12
%U https://doi.org/10.18653/v1/2020.clinicalnlp-1.12
%P 101-110
Markdown (Informal)
[Automatic recognition of abdominal lymph nodes from clinical text](https://aclanthology.org/2020.clinicalnlp-1.12) (Peng et al., ClinicalNLP 2020)
ACL
- Yifan Peng, Sungwon Lee, Daniel C. Elton, Thomas Shen, Yu-xing Tang, Qingyu Chen, Shuai Wang, Yingying Zhu, Ronald Summers, and Zhiyong Lu. 2020. Automatic recognition of abdominal lymph nodes from clinical text. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 101–110, Online. Association for Computational Linguistics.