对于非生物医学领域来说，便宜、快速、足够好，但它是否可用于临床自然语言处理?评估临床试验公告命名实体注释的众包

2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology Pub Date : 2012-09-27 DOI:10.1109/HISB.2012.31

Haijun Zhai, T. Lingren, Louise Deléger, Qi Li, M. Kaiser, Laura Stoutenborough, I. Solti

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引用次数: 3

摘要

基于之前的一般众包研究工作，本研究调查了众包在临床NLP领域用于注释临床试验公告(CTA)语料库中的医疗命名实体和实体链接的可用性。结果表明，众包是一种可行的、廉价的、快速的、实用的方法，可以对医疗命名实体的临床文本(无PHI)进行大规模注释。众包程序代码公开发布。

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Cheap, Fast, and Good Enough for the Non-biomedical Domain but is It Usable for Clinical Natural Language Processing? Evaluating Crowdsourcing for Clinical Trial Announcement Named Entity Annotations

Building upon previous work from the general crowdsourcing research, this study investigates the usability of crowdsourcing in the clinical NLP domain for annotating medical named entities and entity linkages in a clinical trial announcement (CTA) corpus. The results indicate that crowdsourcing is a feasible, inexpensive, fast, and practical approach to annotate clinical text (without PHI) on large scale for medical named entities. The crowdsourcing program code was released publicly.

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来源期刊

2012 IEEE Second International Conference on Healthcare Informatics, Imaging and Systems Biology

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