Cancer Registry Information Extraction via Transfer Learning

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, Sheau-Fang Yang


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.
Anthology ID:
2020.clinicalnlp-1.22
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:
201–208
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.22
DOI:
10.18653/v1/2020.clinicalnlp-1.22
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.22.pdf