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首页> 外文期刊>Concurrency, practice and experience >Graph-based tag recommendations using clusters of patients in clinical decision support system
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Graph-based tag recommendations using clusters of patients in clinical decision support system

机译：基于图的标签推荐，使用临床决策支持系统中的患者簇

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Summary To support health professionals in making decisions, CDSS are developed to manage the patients' EHR, improve the way of diagnosis, and treatment of diseases. The process of analyzing EHRs is based on reading free‐text notes. However, it spends time and physicians' efforts. In this case, the most used solution is describing the EHRs with shortcut tags, representing pathologies or diseases, which are well‐defined and meaningful information. Still, this solution remains insufficient. The exploration of the relationship between those tags, the EHRs and their belonging patients will improve the analysis and then the CDSS. In this paper, we present a graph‐based tag recommendation approach that suggests relevant tags (diseases and pathologies) by analyzing the tagged medical images. We use graph analytics to generate graphs of tags, patients, and images by inspecting similar medical images descriptive. We have also created sub‐communities of patients with the same diseases by applying the Louvain clustering method. The tag recommendation aims to enhance the computer‐aided diagnosis in medical imaging. The tag recommendation approach will allow radiologists to detect and interpret invisible diseases of the underlying anatomical structure. It will also help in early revealing and diagnosis. The dataset ChestX‐Ray14 has been conducted to evaluate and test the accuracy and effectiveness of the proposed approach. Future perspective will focus on the deployment of our proposal within a Moroccan e‐health project.
机译：总结，为了支持卫生专业人员在做出决定时，开发CDSS以管理患者的EHR，提高诊断方式和疾病的治疗方法。分析EHRS的过程基于阅读自由文本备注。但是，它花了时间和医生的努力。在这种情况下，最常用的解决方案正在描述具有快捷标记的EHR，代表病理或疾病，这些疾病是定义明确和有意义的信息。尽管如此，这种解决方案仍然不足。这些标签之间关系的探索，EHRS及其归属患者将改善分析，然后改善CDS。在本文中，我们介绍了一种基于图形的标签推荐方法，其通过分析标记的医学图像来提出相关标签（疾病和病理）。我们使用图形分析来通过检查类似的医学图像来生成标签，患者和图像的图形。我们还通过应用Louvain聚类方法创建了患者的患者的亚群。标签推荐旨在增强医学成像的计算机辅助诊断。标签推荐方法将允许放射科医师检测和解释潜在的解剖结构的无形疾病。它还将有助于早期揭示和诊断。已经进行了数据集Chestx-Ray14以评估和测试所提出的方法的准确性和有效性。未来的视角将侧重于在摩洛哥电子卫生项目中部署我们的提案。

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