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首页> 外文期刊>BMC Medical Informatics and Decision Making >Study on structured method of Chinese MRI report of nasopharyngeal carcinoma
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Study on structured method of Chinese MRI report of nasopharyngeal carcinoma

机译：鼻咽癌患者鼻咽癌的结构化方法研究

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摘要

Image text is an important text data in the medical field at it can assist clinicians in making a diagnosis. However, due to the diversity of languages, most descriptions in the image text are unstructured data. The same medical phenomenon may also be described in various ways, such that it remains challenging to conduct text structure analysis. The aim of this research is to develop a feasible approach that can automatically convert nasopharyngeal cancer reports into structured text and build a knowledge network. In this work, we compare commonly used named entity recognition (NER) models, choose the optimal model as our triplet extraction model, and present a Chinese structuring algorithm. Finally, we visualize the results of the algorithm in the form of a knowledge network of nasopharyngeal cancer. In NER, both accuracy and recall of the BERT-CRF model reached 99%. The structured extraction rate is 84.74%, and the accuracy is 89.39%. The architecture based on recurrent neural network does not rely on medical dictionaries or word segmentation tools and can realize triplet recognition. The BERT-CRF model has high performance in NER, and the triplet can reflect the content of the image report. This work can provide technical support for the construction of a nasopharyngeal cancer database.
机译：图像文本是医疗领域的重要文本数据，可以帮助临床医生进行诊断。但是，由于语言的多样性，图像文本中的大多数描述都是非结构化数据。还可以以各种方式描述相同的医学现象，使得进行文本结构分析仍然具有挑战性。本研究的目的是制定一种可行的方法，可以自动将鼻咽癌报告转化为结构性文本并建立知识网络。在这项工作中，我们比较常用的命名实体识别（NER）模型，选择最佳模型作为我们的三联提取模型，并呈现了中国结构化算法。最后，我们以鼻咽癌知识网络的形式可视化算法的结果。在ner中，BERT-CRF模型的精度和召回均达到99％。结构化提取率为84.74％，准确度为89.39％。基于经常性神经网络的架构不依赖于医疗词典或单词分割工具，并且可以实现三重态识别。 BERT-CRF模型在NER中具有高性能，三联体可以反映图像报告的内容。这项工作可以为建造鼻咽癌数据库提供技术支持。

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