指南
计算机科学
背景(考古学)
集合(抽象数据类型)
排名(信息检索)
循证医学
情报检索
质量(理念)
数据科学
循证实践
梅德林
数据挖掘
医学
替代医学
认识论
哲学
病理
古生物学
生物
程序设计语言
法学
政治学
作者
Qing Hu,Zhisheng Huang,Annette ten Teije,Frank van Harmelen,M. Scott Marshall,André Dekker
标识
DOI:10.5220/0005698902820289
摘要
Evidence-based Medical guidelines are developed based on the best available evidence in biomedical science and clinical practice. Such evidence-based medical guidelines should be regularly updated, so that they can optimally serve medical practice by using the latest evidence from medical research. The usual approach to detect such new evidence is to use a set of terms from a guideline recommendation and to create queries for a biomedical search engine such as PubMed, with a ranking over a selected subset of terms to search for relevant new evidence. However, the terms that appear in a guideline recommendation do not always cover all of the information we need for the search, because the contextual information (e.g. time and location, user profile, topics) is usually missing in a guideline recommendation. Enhancing the search terms with contextual information would improve the quality of the search results. In this paper, we propose a topic-centric approach to detect new evidence for updating evidence-based medical guidelines as a context-aware method to improve the search. Our experiments show that this topic centric approach can find the goal evidence for 12 guideline statements out of 16 in our test set, compared with only 5 guideline statements that were found by using a non-topic centric approach.
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