Foufi Vasiliki, Lanteri Sébastien, Gaudet-Blavignac Christophe, Remy Pascal, Montet Xavier, Lovis Christian
Division of Medical Information Sciences Geneva University Hospitals and University of Geneva.
ESIEE Paris.
Stud Health Technol Inform. 2018;255:210-214.
The aim of this work is to develop and validate an automatic annotation tool for the detection and bone localization of scaphoid fractures in radiology reports. To achieve this goal, a rule-based method using a Natural Language Processing (NLP) tool was applied. Finite state automata were constructed to detect, classify and annotate reports. An evaluation of the method on a manually annotated dataset has shown 96,8% of total match.
这项工作的目的是开发并验证一种用于在放射学报告中检测舟状骨骨折并进行骨骼定位的自动注释工具。为实现这一目标，应用了一种使用自然语言处理（NLP）工具的基于规则的方法。构建了有限状态自动机来检测、分类和注释报告。在一个人工注释数据集上对该方法进行的评估显示总匹配率为96.8%。
