网络获取技术在循证护理信息资源检索中的应用研究

目的 加强循证护理信息资源的获取与利用，促进循证护理的发展。方法 文献复习与信息资源检索。结果生物医学文献数据库、搜索引擎、元搜索引擎、电子期刊、重要网站都能获取网络循证护理信息资源。结论 生物医学文献数据库、搜索引擎、元搜索引擎、电子期刊、重要网站都是获取循证护理信息资源的非常重要的途径，传授这些信息资源获取的方法和技巧，是医学信息人员的重要责任。加强护理人员与医学图书情报人员的合作将有利于推动循证护理实践的开展。     

Comparative Analysis of Medical Web Search Using Generalized vs. Niche Technologies

One increasingly popular solution to the problem of information overload is the niche search engine, designed as a customized knowledge management tool to meet the needs of information seekers with similar interests. By limiting web crawling to a specific subject area, the niche engine is able to crawl deeper and more discreetly than commercial counterparts. This exploratory technology assessment study sought to examine differences in information search and retrieval patterns between commercial and niche search engines in areas of debated or uncertain healthcarer treatment. Findings suggest that while information found within the niche search is generally more trustworthy, niche searches leave out many reliable sources which are retrievable through commercial search engines. Also, consumer-friendly (non-technical) resources were found to be more prevalent using commercial search engines, suggesting greater relevance for the layperson within this domain.     

中文搜索引擎在医学信息检索中的对比研究

选用百度、搜狗、中文谷歌、雅虎中国4种中文搜索引擎,分别对其医学信息的检索效果进行分析研究,包括索引深度、查准率、相关性等.结果表明,检索医学信息较好的中文搜索引擎是中文谷歌和百度.     

生物医学本体工程进展

近年来,领域本体在生物医学钡域广泛应用,在知识的获取、管理和检索中发挥了积极作用。通过文献调研找出了牛物医学领域中的小体,选取其中的代表件本体进行调研与分析,并讨论了存在的问题和发展趋势。     

网络信息资源组织中的医学搜索引擎

本文阐述了网络信息资源、网络信息资源组织、搜索引擎的定义、特点以及彼此之间的相互关系，从医学专业的特殊性和专深性说明了医学搜索引擎产生的必要性；着重介绍了医学搜索引擎的类型及目前比较常用的中英文医学搜索引擎，提出医学搜索引擎将发展成为最具前途的医学网络信息资源组织方式。     

MIRASS: Medical Informatics Research Activity Support System Using Information Mashup Network

The advancement of information technology has facilitated the automation and feasibility of online information sharing. The second generation of the World Wide Web (Web 2.0) enables the collaboration and sharing of online information through Web-serving applications. Data mashup, which is considered a Web 2.0 platform, plays an important role in information and communication technology applications. However, few ideas have been transformed into education and research domains, particularly in medical informatics. The creation of a friendly environment for medical informatics research requires the removal of certain obstacles in terms of search time, resource credibility, and search result accuracy. This paper considers three glitches that researchers encounter in medical informatics research; these glitches include the quality of papers obtained from scientific search engines (particularly, Web of Science and Science Direct), the quality of articles from the indices of these search engines, and the customizability and flexibility of these search engines. A customizable search engine for trusted resources of medical informatics was developed and implemented through data mashup. Results show that the proposed search engine improves the usability of scientific search engines for medical informatics. Pipe search engine was found to be more efficient than other engines.     

An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literature and search engines to query such data. While most search engine research is performed in academia with rigorous evaluation, major commercial companies dominate the web search market. Thus, it is expected that commercial pandemic-specific search engines will gain much higher traction than academic alternatives, leading to questions about the empirical performance of these tools. This paper seeks to empirically evaluate two commercial search engines for COVID-19 (Google and Amazon) in comparison with academic prototypes evaluated in the TREC-COVID task. We performed several steps to reduce bias in the manual judgments to ensure a fair comparison of all systems. We find the commercial search engines sizably underperformed those evaluated under TREC-COVID. This has implications for trust in popular health search engines and developing biomedical search engines for future health crises.     

Tracing medical information over the Internet

The Internet became with do doubt a huge and valuable source of information for researchers. The wealth of information on the Internet is second to none and medical information is no exception. Yet with the vast expansion of the Internet and the World Wide Web in specie, to find the kind of information one is looking for, he/she needs to browse thousands of web sites and the experience would be like digging into a stack of hay looking for a needle. That's why search engines and subject indexes, as means to overcome this problem, were introduced and grew so rapidly. In general, there are three approaches to retrieve data from the World Wide Web; the subject directories, search engines and detailed subject indexes. However, there is no single comprehensive search engine or directory and it is recommended to use more than one with different keywords and synonymous.     

面向中药新药研发的语义搜索系统

为解决中药新药研发中的信息集成和检索问题,设计并实现了语义搜索系统TCMSearch。为实现分布式、异构数据库的语义集成和一致性访问,提出语义视图,来定义关系型数据库与领域本体之间的模式映射。该系统根据关系型数据库的语义视图,将用户提出的语义查询重写为结构查询语言（SQL）查询,再分派给各个关系型数据库,最终将查询结果进行语义封装。它还基于本体构建文本内容的语义索引,从而实现了基于概念的内容检索。这些本体驱动的方法,使该系统与关键词搜索系统相比,具有更高的查准率与查全率。该系统已成功部署,它基于一个大型中药领域本体,通过Web方式为中药领域专家提供智能搜索服务。     

A Multiagent Architecture for Developing Medical Information Retrieval Agents

Information that is available on the world wide web (WWW) is already more vast than can be comprehensibly studied by individuals and this quantity is increasing at a staggering pace. The quality of service delivered by physicians is dependent on the availability of current information. The agent paradigm offers a means for enabling physicians to filter information and retrieve only information that is relevant to current patient treatments. As with many specialized domains, agent-based information retrieval in medical domains must satisfy several domain-dependent constraints. A multiple agent architecture is developed and described in detail to efficiently provide agent-based information retrieval from the WWW and other explicit information resources. A simulation of the proposed multiple agent architecture shows a 97% decrease in information overload and an 85% increase in information relevancy over existing meta-search tools (with even larger gains over standard search engines).     

信息技术在循证医学教学中的应用

在循证医学EBM教学中引入信息技术中的互联网、移动互联网、社交网络、网络搜索引擎,改进EBM教学方法,达到扩充医学证据范围、随时随地快速检索、即时讨论分析的目的,从而提升教学质量.     

3种中文图书搜索引擎搜索特性的比较

Cooke图书搜索、百度图书搜索、读秀学术搜索是3种常用的图书搜索引擎。比较分析3种图书搜索引擎的检索方式、检索效率、检索结果及读者参与机制等,使用户更好地了解3种图书搜索引擎的特点与搜索特性,从而更好地利用。     

面向医学学科领域的检索词库构建

分析专业数据库和搜索引擎在进行文献检索时存在的问题及构建医学学科领域检索词库的原因,以教育部科技查新站2014年以来的科技查新报告为数据源,建立经过科技查新和学科专家共同审验通过的检索词库。该词库可应用于科技查新、文献检索教学等领域,还可推广到其他领域。     

Bat-Inspired Algorithm Based Query Expansion for Medical Web Information Retrieval

With the increasing amount of medical data available on the Web, looking for health information has become one of the most widely searched topics on the Internet. Patients and people of several backgrounds are now using Web search engines to acquire medical information, including information about a specific disease, medical treatment or professional advice. Nonetheless, due to a lack of medical knowledge, many laypeople have difficulties in forming appropriate queries to articulate their inquiries, which deem their search queries to be imprecise due the use of unclear keywords. The use of these ambiguous and vague queries to describe the patients’ needs has resulted in a failure of Web search engines to retrieve accurate and relevant information. One of the most natural and promising method to overcome this drawback is Query Expansion. In this paper, an original approach based on Bat Algorithm is proposed to improve the retrieval effectiveness of query expansion in medical field. In contrast to the existing literature, the proposed approach uses Bat Algorithm to find the best expanded query among a set of expanded query candidates, while maintaining low computational complexity. Moreover, this new approach allows the determination of the length of the expanded query empirically. Numerical results on MEDLINE, the on-line medical information database, show that the proposed approach is more effective and efficient compared to the baseline.     

获取网上医学英文期刊免费全文的方法

介绍获取网上医学英文期刊免费全文的5种方法。它们是因特网免费全文期刊信息的网站，医学搜索引擎，期刊主办机构或出版机构的网站，搜索引擎查出期刊的网络版或电子版的网址，网络上的信息服务机构如图书馆对电子期刊的收集、整理、链接等。     

GOPubMed:基于GO和MeSH的信息检索与分析研究

GOPubMed是一种基于PubMed的结果可视化和后处理类型的智能搜索引擎.从工作原理、关键技术以及扩展功能3个方面对其性能进行解析.研究显示,GOPubMed利用基于语义网的语义分类工具--GO(Gene Ontology,基因本体)和MeSH,对PubMed检索结果进行分类,帮助用户快速地找出最相关的命中文献,实现语义网与生物医学信息检索的完美结合,并能对检索结果从多角度进行可视化统计分析.     

A Firefly Algorithm-based Approach for Pseudo-Relevance Feedback: Application to Medical Database

The difficulty of disambiguating the sense of the incomplete and imprecise keywords that are extensively used in the search queries has caused the failure of search systems to retrieve the desired information. One of the most powerful and promising method to overcome this shortcoming and improve the performance of search engines is Query Expansion, whereby the user’s original query is augmented by new keywords that best characterize the user’s information needs and produce more useful query. In this paper, a new Firefly Algorithm-based approach is proposed to enhance the retrieval effectiveness of query expansion while maintaining low computational complexity. In contrast to the existing literature, the proposed approach uses a Firefly Algorithm to find the best expanded query among a set of expanded query candidates. Moreover, this new approach allows the determination of the length of the expanded query empirically. Experimental results on MEDLINE, the on-line medical information database, show that our proposed approach is more effective and efficient compared to the state-of-the-art.     

Dynamic Tables: An Architecture for Managing Evolving,Heterogeneous Biomedical Data in Relational Database Management Systems

Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute–value-based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques. The authors propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. The authors provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design; (2) past approaches used by biomedical informatics researchers; and (3) their sparse, column-store architecture. The performance results show that their architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.