Hong-Jie Dai, Chien-Chang Chen, Tatheer Hussain Mir, Ting-Yu Wang, Chen-Kai Wang, Ya-Chen Chang, Shu-Jung Yu, Yi-Wen Shen, Cheng-Jiun Huang, Chia-Hsuan Tsai, Ching-Yun Wang, Hsiao-Jou Chen, Pei-Shan Weng, You-Xiang Lin, Sheng-Wei Chen, Ming-Ju Tsai, Shian-Fei Juang, Su-Ying Wu, Wen-Tsung Tsai, Ming-Yii Huang, Chih-Jen Huang, Chih-Jen Yang, Ping-Zun Liu, Chiao-Wen Huang, Chi-Yen Huang, William Yu Chung Wang, Inn-Wen Chong, Yi-Hsin Yang
Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed...
December 2024: Computational and Structural Biotechnology Journal