# Question Answering based Clinical Text Structuring Using Pre-trained Language Model

Source: [Question Answering based Clinical Text Structuring Using Pre-trained Language Model](<https://www.sota2.com/research/paper/68fa66941e209131cea11146>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:53:21.106Z

Authors: Jiahui Qiu, Yangming Zhou, Zhiyuan Ma, Tong Ruan, Jinlin Liu, Jing Sun

Published: 2019-08-19T06:21:29.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1908.06606>)

[PDF](<https://arxiv.org/pdf/1908.06606.pdf>)