School of Pharmacy, University of Otago, Dunedin, New Zealand.
J Clin Pharm Ther. 2011 Aug;36(4):504-12. doi: 10.1111/j.1365-2710.2010.01204.x. Epub 2010 Oct 26.
Exhaustive literature searching is a core requirement for developing guidelines for evidence-based practice. MEDLINE is typically used. Searching requires the user to identify appropriate search terms, called Medical Subject Headings (MeSH) and refine the search to retrieve relevant articles. The objective of this study was to develop and test a learning algorithm for conducting a thorough literature search.
A learning algorithm to effectively utilize MeSH terms is presented. This algorithm creates combinations of available MeSH terms from which a search is conducted. The algorithm was applied to search MEDLINE (January 1950 to Janaury 2008) focusing on the impact of pharmaceutical care in HIV-infected patients. The number of relevant articles retrieved from the learning algorithm search was then compared against a static search with a fixed set of keywords implemented by an independent user.
The learning algorithm retrieved 1670 articles with six relevant articles identified. The static search retrieved a total of 49 articles, with three being relevant. These three articles were also located from the learning algorithm-based search. WHAT IS KNOWN AND CONCLUSION: Performing a literature search for retrieving evidence-based studies can be a daunting and error-prone process. The introduction of automatic, learning tools for searching is desirable and we present a possible approach.
全面的文献搜索是制定循证实践指南的核心要求。通常使用 MEDLINE。搜索需要用户识别合适的搜索词，称为医学主题词（MeSH），并细化搜索以检索相关文章。本研究的目的是开发和测试一种用于进行全面文献搜索的学习算法。
提出了一种有效利用 MeSH 术语的学习算法。该算法从可用的 MeSH 术语中创建组合，进行搜索。该算法应用于搜索 MEDLINE（1950 年 1 月至 2008 年 1 月），重点关注药物治疗在 HIV 感染患者中的影响。然后，将从学习算法搜索中检索到的相关文章数量与独立用户实施的固定关键字集的静态搜索进行比较。
学习算法检索到 1670 篇文章，其中有 6 篇相关文章。静态搜索总共检索到 49 篇文章，其中 3 篇相关。这三篇文章也可以从基于学习算法的搜索中找到。
进行检索循证研究的文献搜索可能是一项令人生畏且容易出错的过程。引入自动、学习型搜索工具是可取的，我们提出了一种可能的方法。
