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首页> 外文会议>International workshop on process-oriented information systems in health-care;International workshop on knowledge representation for health care >Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering
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Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering

机译：通过日记过滤检测基于证据的医学指南的新证据

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Evidence-based medical guidelines are systematically developed recommendations with the aim to assist practitioner and patients decisions regarding appropriate health care for specific clinical circumstances, and are based on evidence described in medical research papers. Evidence-based medical guidelines should be regularly updated, such that they can serve medical practice using based on the latest medical research evidence. A usual approach to detecting new evidences is to use a set of terms which appear in a guideline conclusion or recommendation and create queries over a bio-medical search engine such as PubMed with a ranking over a selected subset of terms to search for relevant new research papers. However, the sizes of the found relevant papers are usually very large (i.e. over a few hundreds, even thousands), which results in a low precision of the search. This makes it for medical professionals quite difficult to find which papers are really interesting and useful for updating the guideline. We propose a filtering step to decrease the number of papers. More exactly we are interested in the question if we can reduce the number of papers with no or a slightly lower recall. A plausible approach is to introduce journal filtering, such that evidence appear in those top journals are preferred. In this paper, we extend our approach of detecting new papers for updating evidence-based medical guideline with a journal filtering step. We report our experiments and show that (1) the method with journal filtering can indeed gain a large reduction of the number of papers (69.73%) with a slightly lower recall (14.29%); (2) we show that the journal filtering method keeps relatively more high level evidence papers (category A) and removes all the low level evidence papers (category D).
机译：循证医学指南是系统开发的建议，旨在帮助从业者和患者就特定临床情况做出有关适当医疗保健的决定，并基于医学研究论文中描述的证据。循证医学指南应定期更新，以便可以根据最新医学研究证据为医学实践服务。检测新证据的常用方法是使用出现在指南结论或建议中的一组术语，并通过生物医学搜索引擎（例如PubMed）创建查询，并对所选术语的子集进行排名，以搜索相关的新研究文件。然而，找到的相关论文的大小通常非常大（即，几百甚至几千），这导致搜索精度低。这使得医学专业人员很难找到哪些论文真正有趣并且对于更新指南很有用。我们提出了一个过滤步骤以减少论文数量。更确切地说，我们对是否可以减少没有召回或召回率略低的论文数量感兴趣。一种可行的方法是引入日记帐过滤，以使出现在那些顶级日记帐中的证据更为可取。在本文中，我们扩展了检测新论文的方法，并通过日记过滤步骤更新了循证医学指南。我们报告了我们的实验结果，结果表明：（1）使用日记过滤的方法确实可以大幅减少论文数量（69.73％），而召回率则略低（14.29％）; （2）我们表明，日记过滤方法保留了相对较高级别的证据文件（类别A），并删除了所有较低级别的证据文件（类别D）。

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