Philips Research Europe, Prof. Holstlaan 4, 5656AA, Eindhoven, the Netherlands.
J Digit Imaging. 2012 Apr;25(2):240-9. doi: 10.1007/s10278-011-9411-0.
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.
在本文中，我们描述并评估了一个从放射学报告中提取临床发现和身体部位并对其进行关联的系统。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。一个轻量级推理引擎从 MedLEE 的语义表示中提取临床发现和身体部位，并对其进行关联。我们的研究对于将现有自然语言处理软件嵌入更大系统的研究具有说明性。我们基于神经和乳房放射学报告语料库手动创建了一个标准参考。该标准参考用于评估所提出的系统及其模块的精度和召回率。我们的结果表明，我们系统的精度明显优于其召回率（82.32-91.37% 对 35.67-45.91%）。我们进行了错误分析，并在此讨论了给定其召回率和精度性能的系统的实际可用性。
