BACKGROUND Radiology reports are a permanent record of patient's health information often used in clinical practice and research. Reading radiology reports is common for clinicians and radiologists. However, it is laborious and time-consuming when the amount of reports to be read is large. Assisting clinicians to locate and assimilate the key information of reports is of great significance for improving the efficiency of reading reports. There are few studies on information extraction from Chinese medical texts and its application in radiology information systems (RIS) for efficiency improvement. OBJECTIVES The purpose of this study was to explore methods for extracting, grouping, ranking, delivering, and displaying medical-named entities in radiology reports which can yield efficiency improvement in RISs. METHODS A total of 5,000 reports were obtained from two medical institutions for this study. We proposed a neural network model called Multi-Embedding-BGRU-CRF (bidirectional gated recurrent unit-conditional random field) for medical-named entity recognition and rule-based methods for entity grouping and ranking. Furthermore, a methodology for delivering and displaying entities in RISs was presented. RESULTS The proposed neural named entity recognition model has achieved a good F1 score of 95.88%. Entity ranking achieved a very high accuracy of 99.23%. The weakness of the system is the entity grouping approach which yield accuracy of 91.03%. The effectiveness of the overall solution was proved by an evaluation task performed by two clinicians based on the setup of actual clinical practice. CONCLUSIONS The neural model shows great potential in extracting medical-named entities from radiology reports, especially for languages, that lack lexicons and natural language processing tools. The pipeline of extracting, grouping, ranking, delivering, and displaying medical-named entities could be a feasible solution to enhance RIS functionality by information extraction. The integration of information extraction and RIS has been demonstrated to be effective in improving the efficiency of reading radiology reports.

中文翻译：

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将信息提取引入放射学信息系统以提高阅读报告的效率。

背景技术放射线报告是经常在临床实践和研究中使用的患者健康信息的永久记录。阅读放射学报告对于临床医生和放射科医生是很普遍的。但是，当要读取的报告数量很大时，这既费力又费时。协助临床医生定位和吸收报告的关键信息对于提高阅读报告的效率具有重要意义。从中医文献中提取信息及其在放射信息系统（RIS）中用于提高效率的研究很少。目的本研究的目的是探索在放射学报告中提取，分组，排名，递送和显示医学名称实体的方法，这些方法可以提高RIS的效率。方法总共5，从两家医疗机构获得了000份报告用于该研究。我们提出了一种用于医学命名的实体识别的神经网络模型，称为Multi-Embedding-BGRU-CRF（双向门控递归单元条件随机字段），并提供了基于规则的实体分组和排名方法。此外，提出了一种在RIS中交付和展示实体的方法。结果提出的神经命名实体识别模型取得了良好的F1分数95.88％。实体排名达到了99.23％的非常高的准确性。该系统的弱点是实体分组方法，其产生精度为91.03％。两位临床医生根据实际临床实践建立的评估任务证明了整体解决方案的有效性。结论神经模型显示出从放射学报告中提取医学名称实体的巨大潜力，特别是对于缺少词典和自然语言处理工具的语言。提取，分组，排序，传递和显示医学名称实体的管道可能是通过信息提取来增强RIS功能的可行解决方案。事实证明，信息提取和RIS的集成可有效提高阅读放射学报告的效率。