用于识别药物不良事件的自然语言处理
Natural language processing to identify adverse drug events.
作者信息
Gysbers Michael, Reichley Richard, Kilbridge Peter M, Noirot Laura, Nagarajan Rakesh, Dunagan W Claiborne, Bailey Thomas C
机构信息
BJC HealthCare, Center for Healthcare Quality & Effectiveness; Washington University School of Medicine, St. Louis, MO, USA.
出版信息
AMIA Annu Symp Proc. 2008 Nov 6:961.
We tested and adapted Cancer Text Information Extraction System (caTIES), a publicly available natural language processing tool (NLP), as a method for identifying terms suggestive of adverse drug events (ADEs). Although caTIES was intended to extract concepts from surgical pathology reports, we report that it can successfully be used to search for ADEs on a much broader range of documents.
我们测试并改编了癌症文本信息提取系统（caTIES），这是一种可公开获取的自然语言处理工具（NLP），作为识别提示药物不良事件（ADEs）术语的一种方法。尽管caTIES旨在从外科病理报告中提取概念，但我们报告称它可成功用于在更广泛的文档范围内搜索ADEs。
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