迈向临床笔记中时间推理的通用时间表达模型。
Towards a Generalizable Time Expression Model for Temporal Reasoning in Clinical Notes.
作者信息
Velupillai Sumithra, Mowery Danielle L, Abdelrahman Samir, Christensen Lee, Chapman Wendy W
机构信息
Department of Computer and Systems Sciences (DSV), Stockholm University, Stockholm, Sweden; Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
出版信息
AMIA Annu Symp Proc. 2015 Nov 5;2015:1252-9. eCollection 2015.
Accurate temporal identification and normalization is imperative for many biomedical and clinical tasks such as generating timelines and identifying phenotypes. A major natural language processing challenge is developing and evaluating a generalizable temporal modeling approach that performs well across corpora and institutions. Our long-term goal is to create such a model. We initiate our work on reaching this goal by focusing on temporal expression (TIMEX3) identification. We present a systematic approach to 1) generalize existing solutions for automated TIMEX3 span detection, and 2) assess similarities and differences by various instantiations of TIMEX3 models applied on separate clinical corpora. When evaluated on the 2012 i2b2 and the 2015 Clinical TempEval challenge corpora, our conclusion is that our approach is successful - we achieve competitive results for automated classification, and we identify similarities and differences in TIMEX3 modeling that will be informative in the development of a simplified, general temporal model.
对于许多生物医学和临床任务（如生成时间线和识别表型）而言，准确的时间识别和归一化至关重要。一个主要的自然语言处理挑战是开发和评估一种可泛化的时间建模方法，该方法在不同语料库和机构中都能表现良好。我们的长期目标是创建这样一个模型。我们通过专注于时间表达式（TIMEX3）识别来启动实现这一目标的工作。我们提出了一种系统方法，用于：1）推广用于自动检测TIMEX3跨度的现有解决方案；2）通过应用于不同临床语料库的TIMEX3模型的各种实例来评估异同。在2012年i2b2和2015年临床时间评估挑战语料库上进行评估时，我们的结论是我们的方法是成功的——我们在自动分类方面取得了有竞争力的结果，并且我们识别出了TIMEX3建模中的异同，这将为简化的通用时间模型的开发提供参考。
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