Automatic Extraction of Breast Cancer Information from Clinical Reports

C. Bretschneider, S. Zillner, M. Hammon, P. Gass, Daniel Sonntag
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引用次数: 7

Abstract

The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features. Its underlying information extraction pipeline employs a rule-based grammar approach that is integrated with semantic technologies to determine the relevant information from the patient record. The accuracy of the system, developed with nine thousand clinical documents, reaches accuracy levels of 90% for lymph node status and 69% for the structurally most complex feature, the hormone status.
从临床报告中自动提取乳腺癌信息
大多数临床数据只能在非结构化文本文档中获得。因此,它们在基于数据的临床应用场景中的自动化使用,如质量保证和治疗建议的临床决策支持,受到阻碍,因为它需要大量的人工注释工作。在这项工作中,我们介绍了一个用于自动处理乳房癌患者临床报告的系统,该系统允许自动提取和无缝处理相关文本特征。它的底层信息提取管道采用基于规则的语法方法,该方法与语义技术相结合,从患者记录中确定相关信息。该系统是根据9000份临床文件开发的,对淋巴结状态的准确率达到90%,对结构最复杂的特征——激素状态的准确率达到69%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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