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How to Improve Patient Safety by Text Mining with Medical Incident Reports: Innovative Technologies Using e-Health and Health Technology Assessment

机译:如何通过与医疗事件报告的文本挖掘提高患者安全性:使用电子健康和健康技术评估的创新技术

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We propose a new approach to detect the precarious situation in medical care and solve the communication-gap by analyzing tracking record. We evaluated the degree of similarities between incident documents obtained bottom-up and the links between existing classes granted top-down. We made it possible to evaluate overall similarities regarding incident documents with the techniques of natural language processing and network analysis with more than 20,000 reports. In this research, we evaluated the degree of similarities between incident documents obtained bottom-up and the links between existing classes granted top-down. We made it possible to evaluate overall similarities regarding incident documents by using the method of network analysis. With regard to the background, the results of the analysis demonstrated that compared with abstract and solution, existing classes are inadequate for representing the characteristics of documents and that there is a need to improve classes. Some categorizes by top-down analysis don't reflect the category by the bottom-up analysis. Our results suggest the effectiveness of introducing the network analysis method. We made it possible to analyze the differences of understanding of the incident reports between doctors and nurses. We attempted the consensus building that depended bottom-up by the network analysis.
机译:我们提出了一种通过分析跟踪记录来检测医疗保健中的不稳定情况的新方法。我们评估了事件文档之间获得的相似性,并授予自上而下的现有类之间的链接。我们可以通过使用超过20,000个报告来评估有关事件文档的整体相似性和网络分析。在这项研究中,我们评估了入射文档之间获得了自下而上的相似性,并授予自上而下的现有类之间的链接。我们可以通过使用网络分析方法来评估关于事件文档的整体相似性。关于背景,分析结果表明,与摘要和解决方案相比,现有类别不足以代表文件的特征,并且需要改进类别。通过自上而下的分析,一些分类不会通过自下而上的分析反映该类别。我们的结果表明了介绍网络分析方法的有效性。我们可以分析理解医生和护士之间的事件报告的差异。我们尝试了依赖网络分析自下而上的共识构建。

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