Searching relevant articles from medical resources is an important search task in clinical decision support system. The technical contents and long length of biomedical articles make this search task more complicated than many other search tasks. Previous research on biomedical information retrieval (IR) is typically based on keyword search. In this paper we propose a new approach. Using our approach, a user can use the full article as a query. This reduces the burden on the users and generates an effective automatic query from many more useful search features. In this novel search scenario, we explore in detail several important factors for developing a successful biomedical articles retrieval system, especially focusing on how to automatically convert an article into an effective search query. Specifically, we evaluate the performance of single features with different parameter configurations, as well as combinations of these features using the techniques of learning to rank and rank fusion. Experimental results on PubMed collection show that the introduction field is the most useful feature for transforming a query. Furthermore, our experiments showed that combining multiple features can significantly improve the effectiveness of a search system.

从医学资源中搜索相关文章是临床决策支持系统中的重要搜索任务。生物医学文章的技术内容和较长的篇幅使此搜索任务比许多其他搜索任务更为复杂。先前对生物医学信息检索（IR）的研究通常基于关键字搜索。在本文中，我们提出了一种新的方法。使用我们的方法，用户可以使用全文作为查询。这减轻了用户的负担，并从许多更有用的搜索功能中生成了有效的自动查询。在这种新颖的搜索场景中，我们详细探讨了开发成功的生物医学文章检索系统的几个重要因素，特别是着重于如何将文章自动转换为有效的搜索查询。具体来说，我们使用学习对融合进行排序和排序的技术来评估具有不同参数配置的单个特征的性能，以及这些特征的组合。在PubMed集合上的实验结果表明，引入字段是转换查询最有用的功能。此外，我们的实验表明，组合多种功能可以显着提高搜索系统的效率。