Modular design,application architecture,and usage of a self-service model for enterprise data delivery: The Duke Enterprise Data Unified Content Explorer (DEDUCE)

PurposeData generated in the care of patients are widely used to support clinical research and quality improvement, which has hastened the development of self-service query tools. User interface design for such tools, execution of query activity, and underlying application architecture have not been widely reported, and existing tools reflect a wide heterogeneity of methods and technical frameworks. We describe the design, application architecture, and use of a self-service model for enterprise data delivery within Duke Medicine.MethodsOur query platform, the Duke Enterprise Data Unified Content Explorer (DEDUCE), supports enhanced data exploration, cohort identification, and data extraction from our enterprise data warehouse (EDW) using a series of modular environments that interact with a central keystone module, Cohort Manager (CM). A data-driven application architecture is implemented through three components: an application data dictionary, the concept of “smart dimensions”, and dynamically-generated user interfaces.ResultsDEDUCE CM allows flexible hierarchies of EDW queries within a grid-like workspace. A cohort “join” functionality allows switching between filters based on criteria occurring within or across patient encounters. To date, 674 users have been trained and activated in DEDUCE, and logon activity shows a steady increase, with variability between months. A comparison of filter conditions and export criteria shows that these activities have different patterns of usage across subject areas.ConclusionsOrganizations with sophisticated EDWs may find that users benefit from development of advanced query functionality, complimentary to the user interfaces and infrastructure used in other well-published models. Driven by its EDW context, the DEDUCE application architecture was also designed to be responsive to source data and to allow modification through alterations in metadata rather than programming, allowing an agile response to source system changes.     

我院门诊短信挂号的方案设计与实现

本文介绍了针对我院门诊短信挂号查询和短信挂号预约而设计的一个解决方案并予以实现。在用户使用短信进行挂号预约时,系统使用查询工具Lucene在门诊号别定义和门诊安排记录中进行检索,生成索引文件,返回各种选择信息,完成挂号预约、预约修改和预约取消等功能。     

医疗设备管理信息系统的开发与应用

目的：开发一套针对医学工程科使用的医疗设备管理信息系统。方法：以PowerBuilder9.0和SQL Server2000为开发工具,采用客户机/服务器结构,设计信息录入、浏览、查询和统计、报告生成等窗口模块和功能。结果：客户机与服务器数据交互正常,各模块实现了设计功能。结论：该系统的结构与功能适用于医学工程科的工作实践。     

一种基于NFA查询重写的XML过滤技术

研究了可扩展的标志性语言(XM L)存取控制策略。通过基于不确定的自动机(NFA)的XM L查询重写技术,实现了支持精细粒度的XM L文档存取控制策略。通过构造XM L文档存取控制策略的NFA以及基于NFA的查询语句重写技术,有效地实现了独立于视图的、高效的XM L精细粒度的存取控制。     

P2P数据交换系统的非一致性数据管理

给出了P2P数据交换系统的形式模型,描述了对等体的本地数据一致性约束、对等体间的信任关系以及对等体间的数据交换约束。并在此基础上提出了基于"查询候选数据集"的全局一致性查询处理策略。一个对等体的"查询候选数据集"是满足其本地数据一致性约束和对等体间所有数据交换约束的全局数据库虚拟子视图。提交到一个对等体的查询操作通过在其查询候选集上进行便可以得到全局一致的查询结果。给出了"查询候选数据集"的构造算法。     

基于启发式规则的SPARQL本体查询

提出了基于启发式规则的SPARQL查询。用语言技术平台（LTP）解析出问句的依存分析树（DPT）,然后对问句集的依存分析树进行统计和分析,总结出用于查询三元组抽取的启发式规则,利用这些规则去掉无意义的查询三元组,合并和重组意义不完整的查询三元组。查询三元组经过类映射、实例映射和属性映射得到本体三元组,形成SPARQL查询。用户在B/S结构的查询界面中提交中文自然语言问句,得到中间结果和问句结果。实验结果表明了该方法的有效性。     

Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data,Online News Articles,and Search Queries

BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t₁₉₈=–8.722, P<.001; model 2, t₁₉₈=–5.000, P<.001, model 3, t₁₉₈=–1.882, P=.06; model 4, t₁₉₈=–4.644, P<.001; model 5, t₁₉₈=–4.488, P<.001). In Hubei province, our proposed model''s forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t₁₉₈=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies'' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.     

基于HIS的安全数据交换系统的研究与实现

为了方便患者就医体检,结合医院信息系统的现状,提出并实现了支持多功能查询平台的医院安全数据交换系统的总体设计方案。与现有的＂网闸＂技术相比较,既有效地解决了数据安全和数据共享之间的矛盾,又进行了针对性的研究和开发,使系统更加贴近医院的实际需求。安全数据交换关键技术的解决方案,即系统硬件架构上采用基于FPGA的嵌入式系统,系统软件使用精简的嵌入式linux操作系统,通过在HIS综合查询系统平台中的应用,有效地证明了医用安全数据交换系统设计方案的可行性、可靠性和实用性。