Automatically Correlating Clinical Findings and Body Locations in Radiology Reports Using MedLEE
Automatically Correlating Clinical Findings and Body Locations in Radiology Reports Using MedLEE
复制标题
DOI：
10.1007/s10278-011-9411-0
复制
发表时间：
2012-04-01
影响因子：
4.4
通讯作者：
Qian, Yuechen
中科院分区：
文献类型：
--
作者：
Sevenster, Merlijn;van Ommering, Rob;Qian, Yuechen
在本文中，我们描述和评估了一个系统，该系统从放射学报告中提取临床结果和身体位置，并将它们关联起来。该系统使用医学语言提取和编码系统(MedLEE)将报告的自由文本映射到其内容的结构化语义表示。一个轻量级的推理引擎从MedLEE的语义表示中提取临床结果和身体位置，并将它们关联起来。我们的研究对于将现有的自然语言处理软件嵌入到更大的系统中的研究具有说明性意义。我们根据神经和乳腺放射学报告语料库手动创建了一个标准参考文献。标准参照被用来评价拟议系统及其模块的精确度和召回率。实验结果表明，该系统的准确率明显高于召回率(82.32-91.37%vs.35.67-45.91%)。针对系统的查全率和查准率，我们进行了错误分析，并讨论了系统的实用性。
In this paper, we describe and evaluate a system that extracts clinical findings and body locations from radiology reports and correlates them. The system uses Medical Language Extraction and Encoding System (MedLEE) to map the reports' free text to structured semantic representations of their content. A lightweight reasoning engine extracts the clinical findings and body locations from MedLEE's semantic representation and correlates them. Our study is illustrative for research in which existing natural language processing software is embedded in a larger system. We manually created a standard reference based on a corpus of neuro and breast radiology reports. The standard reference was used to evaluate the precision and recall of the proposed system and its modules. Our results indicate that the precision of our system is considerably better than its recall (82.32-91.37% vs. 35.67-45.91%). We conducted an error analysis and discuss here the practical usability of the system given its recall and precision performance.