SQL查询技术在全样本处方点评中的应用

目的研究建立可操作性、灵活性和实用性的处方评价方法。方法利用SQL查询技术在医院信息系统中提取特定时间段内的处方信息,对全样本处方数据进行多项指标的统计分析。结果共查询2010年全年处方74 366张,执行Select查询语句7次,统计处方评价核心指标6项。统计结果的真实性和工作效率均大大优于传统统计方法。结论 SQL查询技术可用于全样本处方点评,具有快速准确高效的优点。     

标准MedDRA查询浅析及启示

2003年,MedDRA维护与支持服务组织(MSSO)建立了标准MedDRA查询(SMQ)。标准MedDRA查询是若干MedDRA术语的集合及在其基础上建立的查询策略,建立目的是协助识别和查询可能相关的病例。我国药品不良反应病例报告目前超过300万,以此数据为基础评估药品安全性风险时,查询策略的准确性和全面性是科学评估的基础。本文分析SMQ产生背景,研究SMQ的详细设计,并以此为启示,探讨建立不良反应术语标准查询。     

Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study

BackgroundCOVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas.ObjectiveThis infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries.MethodsWe measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19–related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices—the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)—to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people''s concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization.ResultsOur results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures’ level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas).ConclusionsWe quantitatively defined Japanese Yahoo users’ concerns about COVID-19 by using the search counts of COVID-19–related search queries. Our results also showed that the LCI and LCIPP have external validity.     

面向医学信息交换的语义查询系统设计

医疗信息工作者通过制定医学数据交换标准，实现各种纷杂独立的异构医疗信息系统间的数据共享。但由于现实医疗环境的复杂性，存在如HIs、PACS、LIS和OS等异构系统，在这些异构系统中对相同含义的对象可能存在各种不同的语言表达方式，如何识别利用这些信息以提高医疗服务质量，成为备受关注且亟需解决的问题。提出面向医学信息交换的语义查询系统，引入本体的方法来解决语义信息缺乏问题，以更好地实现医疗系统的信息交换和共享。     

基于VB.net的期刊稿件管理系统的设计与开发

目的：开发杂志社学术期刊稿件管理系统,简化稿件管理流程。方法系统后台数据库采用SQL sever2012数据库,使用Visual Basic.net作为前端开发语言,开发出了一套界面人性化,信息查询准确、便捷,数据存储安全可靠的期刊稿件管理系统。结果系统管理员和分社编辑利用该系统可以方便快捷地完成稿件信息的录入、审稿管理、各类查询、统计和打印输出等工作,提高了工作效率,使编辑工作流程实现了自动化。结论该系统满足多种信息需求,符合目前编辑部稿件管理工作的各项要求。     

新发传染病可查询数据库的建立

目的建立新发传染病（EID）可查询数据库,以利于提高医护人员对新发传染病的认识。方法采用J2EE（Java 2 platform,enterprise edition）和关系数据库技术,通过检索和收集可能传人我国的各种EID的临床相关资料,将相关资料编辑成科学、规范的数据清单格式后录人数据库。结果数据库具有跨系统、跨平台特点,有较强的通用性、兼容性、互动性和可更新性,可提供多种检索查询路径。结论所建立的新发传染病查询数据库信息内容较为丰富完整,可以满足各个层面的查询需要。     

基于多维数据模型的中药信息组织方式研究

中药信息组织方式研究是满足中药信息型或分析型数据需求的关键途径。本文总结了中药信息组织方式的研究现状,并应用多维数据模型,设计中药信息的基本维度和概念分层。以信息查询为案例,利用星形模式,设计制作中药信息的多维数据模型,构建中药信息多维查询系统。基于多维数据模型的中药信息组织方式将为中药信息的多角度、多层次查询和利用提供方便。     

基于VB．Net的“军卫一号”通用查询打印系统的开发

目的：开发一套适用于多种数据库的通用查询打印系统。方法：针对“军卫一号”的实际需要，基于VB．Net架构，利用VisualBasic2005开发工具，依照数据库查询的规则编制程序代码，开发了通用查询打印系统，并对相关功能作了简单介绍。结果：该软件的开发与成功应用，使非计算机专业人员可以方便地对多种数据库的数据进行查询，并把查询结果打印出来。结论：该系统界面友好、操作简单，实用性强。在实际应用中，提高了工作效率，减轻了业务操作人员和计算机专业人员的工作压力，具有较高的实用价值。     

A concept-driven biomedical knowledge extraction and visualization framework for conceptualization of text corpora

A number of techniques such as information extraction, document classification, document clustering and information visualization have been developed to ease extraction and understanding of information embedded within text documents. However, knowledge that is embedded in natural language texts is difficult to extract using simple pattern matching techniques and most of these methods do not help users directly understand key concepts and their semantic relationships in document corpora, which are critical for capturing their conceptual structures. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured texts that computers can not interpret very easily. In this paper, we have presented a novel Biomedical Knowledge Extraction and Visualization framework, BioKEVis to identify key information components from biomedical text documents. The information components are centered on key concepts. BioKEVis applies linguistic analysis and Latent Semantic Analysis (LSA) to identify key concepts. The information component extraction principle is based on natural language processing techniques and semantic-based analysis. The system is also integrated with a biomedical named entity recognizer, ABNER, to tag genes, proteins and other entity names in the text. We have also presented a method for collating information extracted from multiple sources to generate semantic network. The network provides distinct user perspectives and allows navigation over documents with similar information components and is also used to provide a comprehensive view of the collection. The system stores the extracted information components in a structured repository which is integrated with a query-processing module to handle biomedical queries over text documents. We have also proposed a document ranking mechanism to present retrieved documents in order of their relevance to the user query.