PDF(Pubmed)
Abstract：
Scientific knowledge is being accumulated in the biomedical literature at an unprecedented pace. The most widely used database with biomedicine-related article abstracts, PubMed, currently contains more than 36 million entries. Users performing searches in this database for a subject of interest face thousands of entries (articles) that are difficult to process manually. In this work, we present an interactive tool for automatically digesting large sets of PubMed articles: PMIDigest (PubMed IDs digester). The system allows for classification/sorting of articles according to different criteria, including the type of article and different citation-related figures. It also calculates the distribution of MeSH (medical subject headings) terms for categories of interest, providing in a picture of the themes addressed in the set. These MeSH terms are highlighted in the article abstracts in different colors depending on the category. An interactive representation of the interarticle citation network is also presented in order to easily locate article \"clusters\" related to particular subjects, as well as their corresponding \"hub\" articles. In addition to PubMed articles, the system can also process a set of Scopus or Web of Science entries. In summary, with this system, the user can have a \"bird\'s eye view\" of a large set of articles and their main thematic tendencies and obtain additional information not evident in a plain list of abstracts.
摘要：
科学知识正以前所未有的速度在生物医学文献中积累。最广泛使用的数据库与生物医学相关的文章摘要，PubMed,目前包含超过3600万个条目。在该数据库中搜索感兴趣的主题的用户面临难以手动处理的数千个条目(文章)。在这项工作中,我们提出了一种交互式工具，用于自动消化大量PubMed文章：PMIDigest（PubMedID消化器）。该系统允许根据不同的标准对物品进行分类/排序，包括文章类型和不同的引文相关数字。它还计算感兴趣类别的MeSH（医学主题词）术语的分布，提供一组主题的图片。这些MeSH术语在文章摘要中以不同的颜色突出显示，具体取决于类别。还提供了文章间引用网络的交互式表示，以便轻松定位与特定主题相关的文章“集群”，以及他们相应的“集线器”文章。除了PubMed文章，系统还可以处理一组Scopus或WebofScience条目。总之,有了这个系统，用户可以拥有大量文章及其主要主题趋势的“鸟瞰”，并获得在简单的摘要列表中不明显的其他信息。
