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Joint Extraction of Entities and Relations of Breast Ultrasound Reports Based on Deep Learning

机译:基于深度学习的乳房超声报告的实体和关系联合提取

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The breast disease has become a common and prevalent disease. Ultrasound is a common way to diagnose breast diseases, so a large number of ultrasound textual reports have been generated which are valuable for breast disease. Recognition of entities and semantic relations through the ultrasound report is the premise to meet the demand of modern clinical system. The paper proposes a joint extraction method of the entity and relation based on text data of breast ultrasound reports, using practical ultrasound reports of patients with breast diseases from a 3A grade hospital in Shanghai, and focuses on an innovative point that is depending on deep learning to extract entities and relations from text data of ultrasound reports. This method uses deep learning to realize the structuralization of breast ultrasound reports to extract partial features of the test on the basis of collecting features from training data. Then, the relation between entities is classified. Finally, the extracted relation classification and the partial features of text are used to improve the accuracy of structuralization. The experiments show the accuracy of text structure increases by nearly 10% via the joint extraction of entity and relation method based on deep learning, comparing with the common structured approach relying on pipeline model, which reaches up to 80.3% in practical text data of ultrasound reports.
机译:乳腺疾病已成为一种常见和普遍的疾病。超声波是诊断乳腺疾病的常见方式,因此已经产生了大量的超声文本报告,这对乳腺疾病有价值。通过超声报告识别实体和语义关系是满足现代临床系统需求的前提。本文提出了基于乳房超声报告文本数据的实体和关系的联合提取方法,利用上海3A级医院乳腺疾病患者的实际超声报告,专注于根据深度学习的创新点从超声报告的文本数据中提取实体和关系。该方法使用深度学习来实现乳房超声报告的结构化,以基于从训练数据收集特征的基础上提取测试的部分特征。然后,实体之间的关系被分类。最后,提取的关系分类和文本的部分特征用于提高结构化的准确性。实验表明,通过基于深度学习的实体和关系方法的联合提取,文本结构的准确性增加了近10%,与依赖于管道模型的常见结构化方法相比,在超声波的实际文本数据中达到高达80.3%的情况下报告。

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