A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports.
A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports.
复制标题
从放射学报告中触发空间触发的混合深度学习方法。
DOI：
10.18653/v1/2020.splu-1.6
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Roberts K
中科院分区：
文献类型：
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作者：
Datta S;Roberts K
放射学报告包含关于患者的重要临床信息，这些信息通常通过空间表达来联系。空间表达式（或触发器）主要用于描述放射学发现或医疗器械相对于某些解剖结构的定位。由于这些表达是放射科医生解释的心理可视化的结果，因此它们是多样和复杂的。本工作的重点是从三个不同的放射学子域自动识别空间表达术语。我们提出了一种基于混合深度学习的NLP方法，该方法包括：1）通过与训练数据中的已知触发项精确匹配来生成一组候选空间触发项，2）应用特定于域的约束来过滤候选触发项，以及3）利用基于BERT的分类器来预测候选触发项是否是真正的空间触发项。结果是有希望的，与标准的基于BERT的序列标记器相比，平均F1测量提高了24个点。
Radiology reports contain important clinical information about patients which are often tied through spatial expressions. Spatial expressions (or triggers) are mainly used to describe the positioning of radiographic findings or medical devices with respect to some anatomical structures. As the expressions result from the mental visualization of the radiologist’s interpretations, they are varied and complex. The focus of this work is to automatically identify the spatial expression terms from three different radiology sub-domains. We propose a hybrid deep learning-based NLP method that includes – 1) generating a set of candidate spatial triggers by exact match with the known trigger terms from the training data, 2) applying domain-specific constraints to filter the candidate triggers, and 3) utilizing a BERT-based classifier to predict whether a candidate trigger is a true spatial trigger or not. The results are promising, with an improvement of 24 points in the average F1 measure compared to a standard BERT-based sequence labeler.