Barrows Jr R C, Busuioc M, Friedman C
Department of Medical Informatics, Columbia University, New York, NY, USA.
Proc AMIA Symp. 2000:51-5.
This paper describes the extraction of structured data relevant to glaucoma diagnosis and progression from visit notes typed as "notational text" by ophthalmologists during patient encounters. We compared two text processing systems: a limited pattern matching system called GDP (Glaucoma Dedicated Parser) and MedLEE, a proven natural language processing system which is in routine use encoding findings from chest radiograph and mammogram reports at the New York-Presbyterian hospital's Columbia-Presbyterian Center. We also evaluated the use of GDP as a preprocessor program to transform notational text into constructions recognizable by MedLEE. These systems have been evaluated according to their recall and precision in the particular task of processing a corpus of "notational text" documents to extract information related to glaucoma disease.
本文描述了从眼科医生在患者会诊期间录入为“注释文本”的就诊记录中提取与青光眼诊断和病情进展相关的结构化数据的过程。我们比较了两个文本处理系统：一个名为GDP（青光眼专用解析器）的有限模式匹配系统，以及MedLEE，一个经过验证的自然语言处理系统，该系统在纽约长老会医院的哥伦比亚长老会中心常规用于对胸部X光片和乳房X光检查报告中的检查结果进行编码。我们还评估了将GDP用作预处理器程序，将注释文本转换为MedLEE可识别的结构的用途。这些系统已根据它们在处理“注释文本”文档语料库以提取与青光眼疾病相关信息的特定任务中的召回率和精确率进行了评估。
