放射学报告中的文本挖掘

Tianxia Gong, Chew Lim Tan, T. Leong, C. Lee, B. Pang, C. C. Tchoyoson Lim, Qi Tian, Suisheng Tang, Zhuo Zhang
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引用次数: 35

摘要

医学文本挖掘近年来受到越来越多的关注。放射学报告包含丰富的信息,描述放射科医生在相关医学图像中对患者的医学状况的观察。然而,由于大多数报告都是自由文本格式,这些报告中包含的有价值的信息不容易访问和使用,除非应用了适当的文本挖掘。在本文中,我们提出了一个文本挖掘系统来提取和利用放射学报告中的信息。该系统由三个主要模块组成:医学发现提取器、报告和图像检索器以及文本辅助图像特征提取器。在评价中,医学发现提取的总精密度和召回率分别为95.5%和87.9%,医学发现所有修饰词的总精密度和召回率分别为88.2%和82.8%。报告图像检索模块和文本辅助图像特征提取模块的总体效果是放射科医生满意的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Text Mining in Radiology Reports
Medical text mining has gained increasing interest in recent years. Radiology reports contain rich information describing radiologistpsilas observations on the patientpsilas medical conditions in the associated medical images. However, as most reports are in free text format, the valuable information contained in those reports cannot be easily accessed and used, unless proper text mining has been applied. In this paper, we propose a text mining system to extract and use the information in radiology reports. The system consists of three main modules: a medical finding extractor, a report and image retriever, and a text-assisted image feature extractor. In evaluation, the overall precision and recall for medical finding extraction are 95.5% and 87.9% respectively, and for all modifiers of the medical findings 88.2% and 82.8% respectively. The overall result of report and image retrieval module and text-assisted image feature extraction module is satisfactory to radiologists.
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