Assessing Pneumonia Identification from Time-Ordered Narrative Reports
Assessing Pneumonia Identification from Time-Ordered Narrative Reports
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发表时间：
2012
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通讯作者：
C. Bejan;Lucy Vanderwende;M. Wurfel;Meliha Yetisgen-Yildiz
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作者：
C. Bejan;Lucy Vanderwende;M. Wurfel;Meliha Yetisgen-Yildiz
在本文中，我们提出了一个自然语言处理系统，可用于医院监测应用程序的目的是识别肺炎患者。为此，我们构建了一系列监督分类器，其中对应于每个分类器的数据集由一组有限的按时间排序的叙述性报告组成。以这种方式，肺炎监测应用将能够基于自患者入院以来已经过去的时间段来为每个患者调用最合适的分类器。与先前提出的用于肺炎识别的基线相比，我们的系统实现了显著更好的结果。
In this paper, we present a natural language processing system that can be used in hospital surveillance applications with the purpose of identifying patients with pneumonia. For this purpose, we built a sequence of supervised classifiers, where the dataset corresponding to each classifier consists of a restricted set of time-ordered narrative reports. In this way the pneumonia surveillance application will be able to invoke the most suitable classifier for each patient based on the period of time that has elapsed since the patient was admitted into the hospital. Our system achieves significantly better results when compared with a baseline previously proposed for pneumonia identification.