首页> 外文会议>2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops >ReDD-Observatory: Using the Web of Data for Evaluating the Research-Disease Disparity
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ReDD-Observatory: Using the Web of Data for Evaluating the Research-Disease Disparity

机译:ReDD观测站:使用数据网评估研究疾病差异

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It is widely accepted that there is a large disparity between the availability of treatment options and the prevalence of diseases all over the world, thus placing individuals in danger. This disparity is partially caused by the restricted access to information that would allow health care and research policy makers to formulate more appropriate measures to mitigate it. Specifically, this shortage of information is caused by the difficulty in reliably obtaining and integrating data regarding the disease burden and the respective research investments. In response to these challenges, the Linked Data paradigm provides a simple mechanism for publishing and interlinking structured information on the Web. In conjunction with the ever increasing data on diseases and health care research available as Linked Data, an opportunity is created to reduce this information gap that would allow for better policy in response to these disparities. In this paper, we present the ReDD-Observatory, an approach for evaluating the Research-Disease Disparity based on the interlinking and integrating of various biomedical data sources. Specifically, we devise a method for representing statistical information as Linked Data and adopt interlinking algorithms for integrating relevant datasets (mainly GHO, Linked CT and PubMed). The assessment of the disparity is then performed with a number of parametrized SPARQL queries on the integrated data substrate. As a consequence, we are for the first time able to provide reliable indicators for the extent of the research-disease disparity in a semi-automated fashion, thus enabling health care professionals and policy makers to make more informed decisions.
机译:众所周度地普遍认为,治疗方案的可用性与世界各地的疾病患病率之间存在较大的差异,从而将个人放在危险之中。这种差异部分由限制获取能够允许医疗保健和研究方针制定者制定更适当措施来减轻措施的信息。具体而言,这种信息短缺是由难以可靠地获得和整合关于疾病负担和各自研究投资的数据。为了响应这些挑战,链接数据范例提供了一种简单的机制,用于发布和互连网络结构化信息。与随着链接数据的可用疾病和医疗保健研究的有关疾病和医疗保健研究的越来越多的数据,创建机会以减少这种信息差距,以允许更好的政策以应对这些差异。在本文中,我们介绍了REDD天文台,一种基于各种生物医学数据源的交互和整合来评估研究疾病差异的方法。具体地,我们设计了一种将统计信息表示为链接数据的方法,并采用用于集成相关数据集的交互算法(主要是GHO,链接CT和PUBMED)。然后在集成数据基板上用多个参数化SPARQL查询进行视差的评估。因此,我们首次以半自动方式为研究疾病差异的程度提供可靠的指标,从而使医疗保健专业人士和决策者能够做出更明智的决定。

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