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A Deep Learning Based Method for Structuring the Chinese Pathological Reports of Lung Specimen

机译：基于深入的学习方法，用于构建肺标本中国病理报告

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摘要

As a kind of electronic reports in text form, the Chinese pathology report of lung specimen contains a large amount of information that is important for clinicians to further analysis and mining. However, various expressions and no fixed format increases the difficulty of extracting and standardizing this information. In this paper, we focus on the extraction of lung lesion locations and the corresponding diagnosis from these reports. And to overcome the difficulties, a structured processing method based on deep learning and the idea of part-of-speech (POS) tagging was proposed. Firstly, the data of lung pathology specimen reports are preprocessed to normalize the medical terms. Secondly, the bidirectional Long Short-Term Memory (Bi-LSTM) neural network is adopted to extract the information of lesion locations and pathological diagnosis from each report. Finally, the obtained information is screened by an information filter method to generate the final structured results. Experimental results on the self-constructed datasets indicated that the proposed method can be beneficial for structuring pathology reports of lung specimen and obtained state-of-the-art results.
机译：作为文本形式的电子报告，肺标本的中国病理报告包含大量信息，对临床医生进一步分析和采矿是重要的。但是，各种表达式和无固定格式增加了提取和标准化此信息的难度。在本文中，我们专注于提取肺病变位置和这些报告的相应诊断。并克服困难，提出了一种基于深度学习的结构化处理方法和语音部分标记（POS）标记。首先，预处理肺部病理学报告数据报告的数据将正常化。其次，采用双向长期记忆（Bi-LSTM）神经网络从每份报告中提取病变位置和病理诊断的信息。最后，通过信息滤波器方法筛选所获得的信息以生成最终结构化结果。在自建数据集上的实验结果表明，该方法可以有利于构建肺标本的病理报告和获得最先进的结果。

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