Abstract：
The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g. is the infection purulent or non-purulent?) are not captured reliably in structured clinical data. With this work, we trained and evaluated a rule-based clinical natural language processing system using 6,576 manually annotated clinical notes derived from the United States Veterans Health Administration (VA) with the goal of automatically extracting and classifying SSTI subtypes from clinical notes. The trained system achieved mention- and document-level performance metrics of the range 0.39 to 0.80 for mention level classification and 0.49 to 0.98 for document level classification.
摘要：
从电子健康记录中可靠地识别皮肤和软组织感染（STTI）对于许多应用非常重要，包括质量改进，临床指南建设，和流行病学分析。然而,在美国,在结构化的临床数据中无法可靠地捕获SSTI的类型（例如感染是化脓性的还是非化脓性的？）。有了这项工作,我们使用来自美国退伍军人健康管理局(VA)的6,576份人工注释的临床笔记对基于规则的临床自然语言处理系统进行了训练和评估,目的是从临床笔记中自动提取和分类SSTI亚型.经过训练的系统实现了提及级分类的0.39至0.80的提及级和文档级性能指标，以及文档级分类的0.49至0.98的范围。
