@Article{信息:doi 10.2196 / / jmir。2043年，作者=“Teodoro, Douglas and Pasche, Emilie and Gobeill, Julien and Emonet, St{\'e}phane and Ruch, Patrick and Lovis, Christian”，标题=“利用语义Web技术构建跨国生物监测网络:需求、设计和初步评估”，期刊=“J Med Internet Res”，年份=“2012”，月份=“五月”，日=“29”，卷=“14”，数=“3”，页=“e73”，关键词=“抗菌药物耐药性;异构数据库;网上信息服务;背景:抗微生物药物耐药性已达到全球令人震惊的水平，并正在成为一个主要的公共卫生威胁。缺乏有效的抗微生物药物耐药性监测系统被认为是耐药性增加的原因之一，这是由于新耐药性和向护理提供者发出警报之间存在滞后。已经制定了几项跟踪耐药性演变的举措。然而，目前还没有公开的有效的实时和不依赖来源的抗菌素耐药性监测系统。目的:设计并实现一种能够提供实时和来源无关的抗微生物药物耐药性监测体系结构，为跨国耐药性监测提供支持。特别是，我们研究了基于语义网络模型的使用，以促进机构间和跨境微生物实验室数据库的集成和互操作性。 Methods: Following the agile software development methodology, we derived the main requirements needed for effective antimicrobial resistance monitoring, from which we proposed a decentralized monitoring architecture based on the Semantic Web stack. The architecture uses an ontology-driven approach to promote the integration of a network of sentinel hospitals or laboratories. Local databases are wrapped into semantic data repositories that automatically expose local computing-formalized laboratory information in the Web. A central source mediator, based on local reasoning, coordinates the access to the semantic end points. On the user side, a user-friendly Web interface provides access and graphical visualization to the integrated views. Results: We designed and implemented the online Antimicrobial Resistance Trend Monitoring System (ARTEMIS) in a pilot network of seven European health care institutions sharing 70+ million triples of information about drug resistance and consumption. Evaluation of the computing performance of the mediator demonstrated that, on average, query response time was a few seconds (mean 4.3, SD 0.1{\texttimes}102 seconds). Clinical pertinence assessment showed that resistance trends automatically calculated by ARTEMIS had a strong positive correlation with the European Antimicrobial Resistance Surveillance Network (EARS-Net) ($\rho$ = .86, P < .001) and the Sentinel Surveillance of Antibiotic Resistance in Switzerland (SEARCH) ($\rho$ = .84, P < .001) systems. Furthermore, mean resistance rates extracted by ARTEMIS were not significantly different from those of either EARS-Net (∆ = {\textpm}0.130; 95{\%} confidence interval --0 to 0.030; P < .001) or SEARCH (∆ = {\textpm}0.042; 95{\%} confidence interval --0.004 to 0.028; P = .004). Conclusions: We introduce a distributed monitoring architecture that can be used to build transnational antimicrobial resistance surveillance networks. Results indicated that the Semantic Web-based approach provided an efficient and reliable solution for development of eHealth architectures that enable online antimicrobial resistance monitoring from heterogeneous data sources. In future, we expect that more health care institutions can join the ARTEMIS network so that it can provide a large European and wider biosurveillance network that can be used to detect emerging bacterial resistance in a multinational context and support public health actions. ", issn="1438-8871", doi="10.2196/jmir.2043", url="//www.mybigtv.com/2012/3/e73/", url="https://doi.org/10.2196/jmir.2043", url="http://www.ncbi.nlm.nih.gov/pubmed/22642960" }