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首页> 外文期刊>Journal of Digital Imaging >Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists
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Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists

机译：乳腺放射学报告中句子之间的跨部门相关性：SVM分类器的开发和针对五名乳腺放射科医生的注释的评估

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Introduce the notion of cross-sectional relatedness as an informational dependence relation between sentences in the conclusion section of a breast radiology report and sentences in the findings section of the same report. Assess inter-rater agreement of breast radiologists. Develop and evaluate a support vector machine (SVM) classifier for automatically detecting cross-sectional relatedness. A standard reference is manually created from 444 breast radiology reports by the first author. A subset of 37 reports is annotated by five breast radiologists. Inter-rater agreement is computed among their annotations and standard reference. Thirteen numerical features are developed to characterize pairs of sentences; the optimal feature set is sought through forward selection. Inter-rater agreement is F-measure 0.623. SVM classifier has F-measure of 0.699 in the 12-fold cross-validation protocol against standard reference. Report length does not correlate with the classifier’s performance (correlation coefficient = −0.073). SVM classifier has average F-measure of 0.505 against annotations by breast radiologists. Mediocre inter-rater agreement is possibly caused by: (1) definition is insufficiently actionable, (2) fine-grained nature of cross-sectional relatedness on sentence level, instead of, for instance, on paragraph level, and (3) higher-than-average complexity of 37-report sample. SVM classifier performs better against standard reference than against breast radiologists’s annotations. This is supportive of (3). SVM’s performance on standard reference is satisfactory. Since optimal feature set is not breast specific, results may transfer to non-breast anatomies. Applications include a smart report viewing environment and data mining.
机译：介绍截面相关性的概念，将其作为乳腺放射学报告结论部分中的句子与该报告的发现部分中的句子之间的信息依赖关系。评估乳房放射科医生的评估者之间的协议。开发和评估支持向量机（SVM）分类器，以自动检测截面相关性。由第一作者从444份乳房放射学报告中手动创建标准参考。五名乳腺放射科医生对37份报告的一部分进行了注释。评价者之间的协议是在其注释和标准参考之间进行计算的。开发了十三种数字特征来表征句子对；通过前向选择寻求最佳功能集。评分者之间的协议是F-measure 0.623。在针对标准参考的12倍交叉验证协议中，SVM分类器的F度量为0.699。报告长度与分类器的效果不相关（相关系数= -0.073）。 SVM分类器相对于乳腺放射科医生的注释具有0.505的平均F值。平庸的评分者之间的协议可能是由以下原因引起的：（1）定义的可行性不足；（2）句子级别（而不是段落级别）的横断面相关性的细粒度性质；以及（3）更高级别的比37个报告样本的平均复杂度高。与标准参考相比，SVM分类器的效果要好于放射线医生的注释。这支持（3）。 SVM的标准参考性能令人满意。由于最佳功能集并非特定于乳房，因此结果可能会转移到非乳房解剖结构。应用程序包括智能报表查看环境和数据挖掘。

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