Roberts Kirk, Rink Bryan, Harabagiu Sanda M, Scheuermann Richard H, Toomay Seth, Browning Travis, Bosler Teresa, Peshock Ronald
The University of Texas at Dallas, Richardson, TX, USA.
AMIA Annu Symp Proc. 2012;2012:779-88. Epub 2012 Nov 3.
Recognizing the anatomical location of actionable findings in radiology reports is an important part of the communication of critical test results between caregivers. One of the difficulties of identifying anatomical locations of actionable findings stems from the fact that anatomical locations are not always stated in a simple, easy to identify manner. Natural language processing techniques are capable of recognizing the relevant anatomical location by processing a diverse set of lexical and syntactic contexts that correspond to the various ways that radiologists represent spatial relations. We report a precision of 86.2%, recall of 85.9%, and F(1)-measure of 86.0 for extracting the anatomical site of an actionable finding. Additionally, we report a precision of 73.8%, recall of 69.8%, and F(1)-measure of 71.8 for extracting an additional anatomical site that grounds underspecified locations. This demonstrates promising results for identifying locations, while error analysis reveals challenges under certain contexts. Future work will focus on incorporating new forms of medical language processing to improve performance and transitioning our method to new types of clinical data.
识别放射学报告中可采取行动的检查结果的解剖位置是医护人员之间关键检查结果沟通的重要组成部分。识别可采取行动的检查结果的解剖位置的困难之一在于，解剖位置并不总是以简单、易于识别的方式表述。自然语言处理技术能够通过处理与放射科医生表示空间关系的各种方式相对应的各种词汇和句法上下文来识别相关的解剖位置。我们报告了提取可采取行动的检查结果的解剖部位时的精确率为86.2%、召回率为85.9%、F(1)值为86.0。此外，我们报告了提取为未明确指定位置提供依据的额外解剖部位时的精确率为73.8%、召回率为69.8%、F(1)值为71.8。这表明在识别位置方面取得了有前景的结果，而错误分析揭示了某些情况下的挑战。未来的工作将集中于纳入新形式的医学语言处理以提高性能，并将我们的方法应用于新型临床数据。
