Multiple features for clinical relation extraction: A machine learning approach.,Journal of Biomedical informatics

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Multiple features for clinical relation extraction: A machine learning approach.
Journal of Biomedical informatics ( IF 4.0 ) Pub Date : 2020-02-03 , DOI: 10.1016/j.jbi.2020.103382
Ilseyar Alimova ₁ , Elena Tutubalina ₂

Affiliation

Relation extraction aims to discover relational facts about entity mentions from plain texts. In this work, we focus on clinical relation extraction; namely, given a medical record with mentions of drugs and their attributes, we identify relations between these entities. We propose a machine learning model with a novel set of knowledge-based and BioSentVec embedding features. We systematically investigate the impact of these features with standard distance- and word-based features, conducting experiments on two benchmark datasets of clinical texts from MADE 2018 and n2c2 2018 shared tasks. For comparison with the feature-based model, we utilize state-of-the-art models and three BERT-based models, including BioBERT and Clinical BERT. Our results demonstrate that distance and word features provide significant benefits to the classifier. Knowledge-based features improve classification results only for particular types of relations. The sentence embedding feature provides the largest improvement in results, among other explored features on the MADE corpus. The classifier obtains state-of-the-art performance in clinical relation extraction with F-measure of 92.6%, improving F-measure by 3.5% on the MADE corpus.

中文翻译：

临床关系提取的多种功能：一种机器学习方法。

关系提取旨在从纯文本中发现有关实体提及的关系事实。在这项工作中，我们专注于临床关系提取。也就是说，给定病历并提及药物及其属性，我们可以确定这些实体之间的关系。我们提出了一种具有新颖的基于知识和BioSentVec嵌入功能的机器学习模型。我们系统地研究了这些功能与基于距离和基于单词的标准功能的影响，对MADE 2018和n2c2 2018共享任务的两个临床文本基准数据集进行了实验。为了与基于特征的模型进行比较，我们利用了最新模型和三个基于BERT的模型，包括BioBERT和Clinical BERT。我们的结果表明，距离和单词特征为分类器提供了明显的好处。基于知识的功能仅针对特定类型的关系才能改善分类结果。句子嵌入功能提供了最大的结果改进，其中包括MADE语料库上的其他探索功能。该分类器以92.6％的F值获得了临床关系提取中的最新技术，在MADE语料库上将F值提高了3.5％。

更新日期：2020-02-03

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