论文标题
微调Ernie用于胸部异常成像标志提取
Fine-tuning ERNIE for chest abnormal imaging signs extraction
论文作者
论文摘要
胸部成像报告描述了胸部射线照相程序的结果。从胸部成像报告中自动提取异常成像标志在临床研究和各种下游医疗任务中具有关键作用。但是，从中国胸部成像报告中提取信息的研究很少。在本文中，我们将胸部异常成像符号提取作为序列标记和匹配问题。在此基础上，我们提出了一个以Ernie为主链的转移的异常成像符号提取器，称为Eason（用CRF进行微调Ernie，用于提取异常迹象），可以解决数据不足的问题。此外，为了将属性（身体部位和程度）分配给序列标记模型结果的相应异常成像符号，我们根据胸部成像报告文本的性质设计了一种简单但有效的TAG2REALATION算法。我们评估了医疗大数据公司提供的语料库的方法，实验结果表明，与其他基线相比，我们的方法可以实现显着且一致的改进。
Chest imaging reports describe the results of chest radiography procedures. Automatic extraction of abnormal imaging signs from chest imaging reports has a pivotal role in clinical research and a wide range of downstream medical tasks. However, there are few studies on information extraction from Chinese chest imaging reports. In this paper, we formulate chest abnormal imaging sign extraction as a sequence tagging and matching problem. On this basis, we propose a transferred abnormal imaging signs extractor with pretrained ERNIE as the backbone, named EASON (fine-tuning ERNIE with CRF for Abnormal Signs ExtractiON), which can address the problem of data insufficiency. In addition, to assign the attributes (the body part and degree) to corresponding abnormal imaging signs from the results of the sequence tagging model, we design a simple but effective tag2relation algorithm based on the nature of chest imaging report text. We evaluate our method on the corpus provided by a medical big data company, and the experimental results demonstrate that our method achieves significant and consistent improvement compared to other baselines.
