ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.

ConText: an algorithm for determining negation, experiencer, and temporal status from clinical reports.
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DOI:
10.1016/j.jbi.2009.05.002
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发表时间:
2009-10
影响因子:
4.5
通讯作者:
Chapman WW
Chapman WW
中科院分区:
医学3区
文献类型:
--
作者:
Harkema H;Dowling JN;Thornblade T;Chapman WW

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在本文中,我们描述了一种称为ConText的算法,用于确定临床报告中提到的临床状况是被否定的、假设的、历史的还是由患者以外的人经历的。该算法根据条件上下文中出现的简单词法线索推断出与这些属性相关的条件的状态。本文提出的算法的讨论和评估解决了一个简单的基于表面的方法是否已经被证明可以很好地用于否定,是否可以成功地转移到临床条件的其他上下文属性,以及这种方法在多大程度上可移植到不同的临床报告类型。在我们的研究中,我们发现ConText在包含否定、历史和假设条件的所有报告类型中都获得了合理到良好的性能。在我们的报告集中很少发现由患者以外的人经历的情况。一个全面的解决方案来确定一个临床状况是历史的还是最近的问题需要超越上下文所获得的表面线索的知识。
In this paper we describe an algorithm called ConText for determining whether clinical conditions mentioned in clinical reports are negated, hypothetical, historical, or experienced by someone other than the patient. The algorithm infers the status of a condition with regard to these properties from simple lexical clues occurring in the context of the condition. The discussion and evaluation of the algorithm presented in this paper address the questions of whether a simple surface-based approach which has been shown to work well for negation can be successfully transferred to other contextual properties of clinical conditions, and to what extent this approach is portable among different clinical report types. In our study we find that ConText obtains reasonable to good performance for negated, historical, and hypothetical conditions across all report types that contain such conditions. Conditions experienced by someone other than the patient are very rarely found in our report set. A comprehensive solution to the problem of determining whether a clinical condition is historical or recent requires knowledge above and beyond the surface clues picked up by ConText.
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