Biomedical Informatics Group, Departamento de Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Boadilla del Monte, 28660 Madrid, Spain.
BMC Med Inform Decis Mak. 2012 Aug 2;12:82. doi: 10.1186/1472-6947-12-82.
Over the past years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for the Medical Informatics (MI) field, so that locating and accessing them currently remains a difficult and time-consuming task.
We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. We define informatics resources as all those elements that constitute, serve to define or are used by informatics systems, ranging from architectures or development methodologies to terminologies, vocabularies, databases or tools. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources' names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different classification schemas by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the classification schemas. The classification algorithm identifies the categories associated with resources and annotates them accordingly. The database is then populated with this data after manual curation and validation.
We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contains 609 resources at the time of writing and is available at http://www.gib.fi.upm.es/eMIR2. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers.
在过去的几年中，医学领域中的信息学资源数量呈指数级增长。虽然已经针对生物信息学（BI）开发了此类资源的特定清单，但医学信息学（MI）领域还没有可比的清单，因此目前定位和访问这些资源仍然是一项困难且耗时的任务。
我们从科学文献中创建了一个 MI 资源库，通过基于网络的服务提供对其内容的免费访问。我们将信息学资源定义为构成、用于定义或由信息学系统使用的所有元素，范围从架构或开发方法到术语、词汇、数据库或工具。从发表在顶级 MI 期刊上的手稿中自动提取描述资源的相关信息。我们使用模式匹配方法来检测资源的名称及其主要特征。检测到的资源根据三个不同的标准进行分类：功能、资源类型和领域。为了方便这些任务，我们通过基于标签和社交标记的新方法构建了三个不同的分类方案。我们采用 MI 研究人员在其出版物中最常使用的术语来创建属于分类方案的概念和层次关系。分类算法识别与资源相关的类别并进行相应的标注。在经过手动整理和验证后，数据库将填充这些数据。
我们创建了一个 MI 资源在线存储库，以帮助研究人员定位和访问最适合执行特定任务的资源。在撰写本文时，数据库包含 609 个资源，可在 http://www.gib.fi.upm.es/eMIR2 上访问。我们将通过考虑进一步的出版物以及用户和资源开发人员的建议，继续扩展可用资源的数量。
