The Chilean Waiting List Corpus: a new resource for clinical Named Entity Recognition in Spanish

P. Baez, F. Villena, Matías Rojas, Manuel Durán, J. Dunstan
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引用次数: 21

Abstract

In this work we describe the Waiting List Corpus consisting of de-identified referrals for several specialty consultations from the waiting list in Chilean public hospitals. A subset of 900 referrals was manually annotated with 9,029 entities, 385 attributes, and 284 pairs of relations with clinical relevance. A trained medical doctor annotated these referrals, and then together with other three researchers, consolidated each of the annotations. The annotated corpus has nested entities, with 32.2% of entities embedded in other entities. We use this annotated corpus to obtain preliminary results for Named Entity Recognition (NER). The best results were achieved by using a biLSTM-CRF architecture using word embeddings trained over Spanish Wikipedia together with clinical embeddings computed by the group. NER models applied to this corpus can leverage statistics of diseases and pending procedures within this waiting list. This work constitutes the first annotated corpus using clinical narratives from Chile, and one of the few for the Spanish language. The annotated corpus, the clinical word embeddings, and the annotation guidelines are freely released to the research community.
智利等候名单语料库:西班牙语临床命名实体识别的新资源
在这项工作中,我们描述了等候名单语料库,包括从智利公立医院等候名单上的几个专业咨询的去识别转诊。900个转诊的子集被手动标注了9029个实体、385个属性和284对具有临床相关性的关系。一名训练有素的医生对这些转诊进行了注释,然后与其他三名研究人员一起对每个注释进行了合并。标注的语料库有嵌套的实体,32.2%的实体嵌入到其他实体中。我们使用这个带注释的语料库来获得命名实体识别(NER)的初步结果。使用biLSTM-CRF架构,使用在西班牙语维基百科上训练的词嵌入以及由该小组计算的临床嵌入,获得了最好的结果。应用于该语料库的NER模型可以在此等待列表中利用疾病和未决程序的统计数据。这项工作构成了第一个使用智利临床叙述的注释语料库,也是为数不多的西班牙语之一。标注的语料库、临床词嵌入和标注指南免费发布给研究社区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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