Assertion modeling and its role in clinical phenotype identification
Assertion modeling and its role in clinical phenotype identification
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DOI：
10.1016/j.jbi.2012.09.001
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发表时间：
2013-02-01
影响因子：
4.5
通讯作者：
Yetisgen-Yildiz, Meliha
中科院分区：
文献类型：
--
作者：
Bejan, Cosmin Adrian;Vanderwende, Lucy;Yetisgen-Yildiz, Meliha
本文介绍了一种方法，断言分类和实证研究的影响，这项任务的表型识别，一个真实的世界的应用在临床领域。断言分类的任务是分配给临床报告中提到的每个医学概念（例如，肺炎，胸痛）特定的断言类别（例如，存在、不存在和可能）。为了提高医疗断言的分类，我们提出了几个新的功能，捕捉特殊提示词的语义属性高度指示一个特定的断言类别。所获得的结果优于目前最先进的结果，这项任务。此外，我们确认的直觉，断言分类有助于显着提高结果的表型识别从自由文本的临床记录。(C)2012 Elsevier Inc. All rights reserved.
This paper describes an approach to assertion classification and an empirical study on the impact this task has on phenotype identification, a real world application in the clinical domain. The task of assertion classification is to assign to each medical concept mentioned in a clinical report (e.g., pneumonia, chest pain) a specific assertion category (e.g., present, absent, and possible). To improve the classification of medical assertions, we propose several new features that capture the semantic properties of special cue words highly indicative of a specific assertion category. The results obtained outperform the current state-of-the-art results for this task. Furthermore, we confirm the intuition that assertion classification contributes in significantly improving the results of phenotype identification from free-text clinical records. (C) 2012 Elsevier Inc. All rights reserved.