Deng Yihan, Denecke Kerstin
Innovation Center Computer Assisted Surgery, University of Leipzig.
Department of Medical Informatics, Bern University of Applied Sciences.
Stud Health Technol Inform. 2016;228:18-22.
Clinical data is often captured in unstructured texts and scattered in different health information systems. This complicates the aggregation of information in the process of clinical decision making. However, having a quick overview and an efficient representation of relevant aspects of a patient's health status are crucial for this process. While accessing patient data and perusing clinical documents, relevant details need to be discovered quickly. In this paper, we introduce an approach to visualize relevant information from clinical documents by tag clouds. The conventional tag clouds visualize the content of a document using the terms they are containing shown in different sizes with the size calculated based on the term frequency. Important facts and diagnostic results with low occurrence in a text may be ignored by this naïve method. In this paper, we therefore adapt the conventional tag clouds by information extraction and a guidelines-based classification schema, so that the clinical concerns can be visualized more correctly. The aspects are extracted according to a classification schema developed by clinical experts. We evaluate the approach on a set of radiology reports for cervical spine treatment.
临床数据通常以非结构化文本的形式记录，并分散在不同的健康信息系统中。这使得临床决策过程中的信息汇总变得复杂。然而，对患者健康状况的相关方面有一个快速的概述和有效的呈现对于这个过程至关重要。在访问患者数据和查阅临床文档时，需要快速发现相关细节。在本文中，我们介绍一种通过标签云可视化临床文档中相关信息的方法。传统的标签云使用文档中包含的术语以不同大小来可视化文档内容，大小根据词频计算。文本中出现频率低的重要事实和诊断结果可能会被这种简单的方法忽略。因此，在本文中，我们通过信息提取和基于指南的分类模式对传统标签云进行了改进，以便更准确地可视化临床关注点。这些方面是根据临床专家制定的分类模式提取的。我们在一组用于颈椎治疗的放射学报告上评估了该方法。
