End-to-End Approach for Structuring Radiology Reports
End-to-End Approach for Structuring Radiology Reports
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DOI：
10.3233/shti200151
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发表时间：
2020-06
影响因子：
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通讯作者：
Kento Sugimoto;Toshihiro Takeda;Shoya Wada;Asuka Yamahata;Shozo Konishi;S. Manabe;Y. Matsumura
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文献类型：
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作者：
Kento Sugimoto;Toshihiro Takeda;Shoya Wada;Asuka Yamahata;Shozo Konishi;S. Manabe;Y. Matsumura
放射学报告包括用于患者护理的各种类型的临床信息。报告也有望有二次用途（例如，临床研究和决策支持系统的发展）。对于二次使用，有必要从报告中提取信息并将其组织为结构化格式。我们的目标是构建一个应用程序，将以自由文本形式编写的放射学报告转换为结构化格式。为此，我们提出了一种端到端方法，该方法由三个要素组成。首先，我们建立了一个神经网络模型，从报告中提取临床信息。我们在胸部x光报告的数据集上做了实验。其次，我们将提取的信息转换为结构化格式。最后，我们构建了一个工具，可以将报表中的术语转换为标准形式。通过我们的端到端方法，我们可以获得一个结构化的放射学数据集，便于二次使用。
Radiology reports include various types of clinical information that are used for patient care. Reports are also expected to have secondary uses (e.g., clinical research and the development of decision support systems). For secondary use, it is necessary to extract information from the report and organize it in a structured format. Our goal is to build an application to transform radiology reports written in a free-text form into a structured format. To this end, we propose an end-to-end method that consists of three elements. First, we built a neural network model to extract clinical information from the reports. We experimented on a dataset of chest X-ray reports. Second, we transformed the extracted information into a structured format. Finally, we built a tool that enabled the transformation of terms in reports to standard forms. Through our end-to-end method, we could obtain a structured radiology dataset that was easy to access for secondary use.