@inproceedings{lin-etal-2020-cancer,
title = "Cancer Registry Information Extraction via Transfer Learning",
author = "Lin, Yan-Jie and
Dai, Hong-Jie and
Zhang, You-Chen and
Wu, Chung-Yang and
Chang, Yu-Cheng and
Lu, Pin-Jou and
Huang, Chih-Jen and
Wang, Yu-Tsang and
Hsieh, Hui-Min and
Chao, Kun-San and
Liu, Tsang-Wu and
Chang, I-Shou and
Yang, Yi-Hsin Connie and
Wang, Ti-Hao and
Liu, Ko-Jiunn and
Chen, Li-Tzong and
Yang, Sheau-Fang",
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-1.22",
doi = "10.18653/v1/2020.clinicalnlp-1.22",
pages = "201--208",
abstract = "A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.",
}
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<abstract>A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.</abstract>
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%0 Conference Proceedings
%T Cancer Registry Information Extraction via Transfer Learning
%A Lin, Yan-Jie
%A Dai, Hong-Jie
%A Zhang, You-Chen
%A Wu, Chung-Yang
%A Chang, Yu-Cheng
%A Lu, Pin-Jou
%A Huang, Chih-Jen
%A Wang, Yu-Tsang
%A Hsieh, Hui-Min
%A Chao, Kun-San
%A Liu, Tsang-Wu
%A Chang, I-Shou
%A Yang, Yi-Hsin Connie
%A Wang, Ti-Hao
%A Liu, Ko-Jiunn
%A Chen, Li-Tzong
%A Yang, Sheau-Fang
%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 lin-etal-2020-cancer
%X A cancer registry is a critical and massive database for which various types of domain knowledge are needed and whose maintenance requires labor-intensive data curation. In order to facilitate the curation process for building a high-quality and integrated cancer registry database, we compiled a cross-hospital corpus and applied neural network methods to develop a natural language processing system for extracting cancer registry variables buried in unstructured pathology reports. The performance of the developed networks was compared with various baselines using standard micro-precision, recall and F-measure. Furthermore, we conducted experiments to study the feasibility of applying transfer learning to rapidly develop a well-performing system for processing reports from different sources that might be presented in different writing styles and formats. The results demonstrate that the transfer learning method enables us to develop a satisfactory system for a new hospital with only a few annotations and suggest more opportunities to reduce the burden of cancer registry curation.
%R 10.18653/v1/2020.clinicalnlp-1.22
%U https://aclanthology.org/2020.clinicalnlp-1.22
%U https://doi.org/10.18653/v1/2020.clinicalnlp-1.22
%P 201-208
Markdown (Informal)
[Cancer Registry Information Extraction via Transfer Learning](https://aclanthology.org/2020.clinicalnlp-1.22) (Lin et al., ClinicalNLP 2020)
ACL
- Yan-Jie Lin, Hong-Jie Dai, You-Chen Zhang, Chung-Yang Wu, Yu-Cheng Chang, Pin-Jou Lu, Chih-Jen Huang, Yu-Tsang Wang, Hui-Min Hsieh, Kun-San Chao, Tsang-Wu Liu, I-Shou Chang, Yi-Hsin Connie Yang, Ti-Hao Wang, Ko-Jiunn Liu, Li-Tzong Chen, and Sheau-Fang Yang. 2020. Cancer Registry Information Extraction via Transfer Learning. In Proceedings of the 3rd Clinical Natural Language Processing Workshop, pages 201–208, Online. Association for Computational Linguistics.