Rubin Daniel, Wang Dan, Chambers Dallas A, Chambers Justin G, South Brett R, Goldstein Mary K
VA Palo Alto Health Care System, Palo Alto, CA;
AMIA Annu Symp Proc. 2010 Nov 13;2010:692-6.
Radiology reports are unstructured free text documents that describe abnormalities in patients that are visible via imaging modalities such as X-ray. The number of imaging examinations performed in clinical care is enormous, and mining large repositories of radiology reports connected with clinical data such as patient outcomes could enable epidemiological studies, such as correlating the frequency of infections to the presence or length of time medical devices are present in patients. We developed a natural language processing (NLP) system to recognize device mentions in radiology reports and information about their state (insertion or removal) to enable epidemiological research. We tested our system using a reference standard of reports that were annotated to indicate this information. Our system performed with high accuracy (recall and precision of 97% and 99% for device mentions and 91-96% for device insertion status). Our methods are generalizable to other types of radiology reports as well as to other information extraction tasks and could provide the foundation for tools that enable epidemiological research exploration based on mining radiology reports.
放射学报告是无结构的自由文本文件，用于描述通过诸如X光等成像方式在患者身上可见的异常情况。临床护理中进行的成像检查数量巨大，挖掘与患者结局等临床数据相关的大量放射学报告库能够开展流行病学研究，比如将感染频率与患者体内医疗设备的存在情况或留置时间相关联。我们开发了一个自然语言处理（NLP）系统，以识别放射学报告中提及的设备及其状态信息（插入或移除），从而支持流行病学研究。我们使用经过注释以表明此信息的报告参考标准对我们的系统进行了测试。我们的系统表现出了很高的准确性（设备提及的召回率和精确率分别为97%和99%，设备插入状态的召回率和精确率为91%-96%）。我们的方法可推广到其他类型的放射学报告以及其他信息提取任务，并可为基于挖掘放射学报告开展流行病学研究探索的工具提供基础。
