A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature.

Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain J Marshall, Ani Nenkova, Byron C Wallace
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Abstract

We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the 'PICO' elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.

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一个包含患者、干预措施和结果的多层次注释的语料库,以支持医学文献的语言处理。
我们提供了一个由5000篇注释丰富的医学文章摘要组成的语料库,这些文章描述了临床随机对照试验。注释包括描述入选患者群体、所研究的干预措施和与之比较的文本跨度的分界,以及测量的结果(“PICO”元素)。这些跨度在更精细的层面上被进一步注释,例如,其中的个体干预被标记并映射到结构化的医学词汇表中。我们从具有不同专业知识和成本水平的不同员工那里获得了注释。我们详细描述了我们的数据收集过程和语料库本身。然后,我们概述了一系列具有挑战性的NLP任务,这些任务将有助于检索医学文献和循证医学实践。
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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