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 tasks and make dataset shareable. A novel model that aims to introduce domain-specific 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.
@article{arxiv.1908.06606,
title = {Question Answering based Clinical Text Structuring Using Pre-trained Language Model},
author = {Jiahui Qiu and Yangming Zhou and Zhiyuan Ma and Tong Ruan and Jinlin Liu and Jing Sun},
journal= {arXiv preprint arXiv:1908.06606},
year = {2019}
}