Radiological reporting generates a large amount of free-text clinical narrative, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed by radiologists of the Italian hospital ASST Spedali Civili di Brescia. At the time of writing, 346 reports have been annotated by a radiologist. Each report is classified according to the schema developed by radiologists and textual evidences are marked in the report. The annotations are then used to train different machine learning based classifiers. We present in this paper a method based on a cascade of classifiers which make use of a set of syntactic and semantic features. By testing the classifiers in cross-validation on manually annotated reports, we obtained a range of accuracy of 81-96%.
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机译:放射学报告产生了大量的自由文本临床叙述,这对于改善临床护理和支持研究可能是有价值的信息来源。必须使用自动技术来分析此类报告,以使放射科医生以汇总形式有效地获取其内容。在本文中,我们根据意大利医院ASST Spedali Civili di Brescia的放射科医生提出的分类方案,重点介绍了胸部计算机断层扫描报告的分类。在撰写本文时,放射科医生已经注释了346份报告。每个报告均根据放射科医生制定的方案进行分类,并且在报告中标记文字证据。注释然后用于训练不同的基于机器学习的分类器。我们在本文中提出了一种基于分类器级联的方法,该方法利用了一组句法和语义特征。通过在人工注释的报告上对交叉分类中的分类器进行测试,我们获得了81-6%的准确度范围。
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