文献类型：期刊文献

英文题名：Question answering based clinical text structuring using pre-trained language model

作者：Qiu, Jiahui[1]; Zhou, Yangming[1]; Ma, Zhiyuan[1]; Ruan, Tong[1]; Liu, Jinlin[1]; Sun, Jing[2]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China

年份：2019

外文期刊名：arXiv

收录：EI(收录号：20200289924)

语种：英文

外文关键词：Computational linguistics - Natural language processing systems

摘要：Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific end-to-end models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QA-CTS) task to unify different specific CTS tasks and make dataset shareable. A novel model that aims to introduce domainspecific features (e.g., clinical named entity information) into pre-trained language model is also proposed for QA-CTS task. Experimental results on Chinese pathology reports collected from Ruijing Hospital demonstrate our presented QA-CTS task is very effective to improve the performance on specific tasks. Our proposed model also competes favorably with strong baseline models in specific tasks. Copyright ? 2019, The Authors. All rights reserved.