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

arXiv: https://arxiv.org/abs/1908.06606 · HTML: https://mlsanity.com/paper/1908.06606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [Question answering systems for health professionals at the point of care -- a systematic review](https://mlsanity.com/paper/2402.01700/) (2024)

## Abstract (arXiv)

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.