Taira R K, Soderland S G, Jakobovits R M
Department of Radiology, Children's Hospital and Regional Medical Center, 4800 Sandpoint Way NE, Mailstop CH-69, Seattle, WA 98105, USA.
Radiographics. 2001 Jan-Feb;21(1):237-45. doi: 10.1148/radiographics.21.1.g01ja18237.
A natural language processor was developed that automatically structures the important medical information (eg, the existence, properties, location, and diagnostic interpretation of findings) contained in a radiology free-text document as a formal information model that can be interpreted by a computer program. The input to the system is a free-text report from a radiologic study. The system requires no reporting style changes on the part of the radiologist. Statistical and machine learning methods are used extensively throughout the system. A graphical user interface has been developed that allows the creation of hand-tagged training examples. Various aspects of the difficult problem of implementing an automated structured reporting system have been addressed, and the relevant technology is progressing well. Extensible Markup Language is emerging as the preferred syntactic standard for representing and distributing these structured reports within a clinical environment. Early successes hold out hope that similar statistically based models of language will allow deep understanding of textual reports. The success of these statistical methods will depend on the availability of large numbers of high-quality training examples for each radiologic subdomain. The acceptability of automated structured reporting systems will ultimately depend on the results of comprehensive evaluations.
开发了一种自然语言处理器，它能自动将放射学自由文本文件中包含的重要医学信息（如检查结果的存在、特征、位置及诊断解读）构建为一种可由计算机程序解释的形式化信息模型。该系统的输入是放射学检查的自由文本报告。该系统不要求放射科医生改变报告风格。统计和机器学习方法在整个系统中被广泛使用。已开发出一个图形用户界面，可用于创建手动标注的训练示例。已解决了实施自动化结构化报告系统这一难题的各个方面，相关技术进展良好。可扩展标记语言正成为在临床环境中表示和分发这些结构化报告的首选句法标准。早期的成功让人希望类似的基于统计的语言模型能实现对文本报告的深入理解。这些统计方法的成功将取决于每个放射学子领域是否有大量高质量的训练示例。自动化结构化报告系统的可接受性最终将取决于综合评估的结果。
