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Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering

  • Qing HuEmail author
  • Zhisheng Huang
  • Annette ten Teije
  • Frank van Harmelen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10096)

Abstract

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).

Keywords

Relevant Paper Guideline Statement Lower Recall Provenance Information General Medical Journal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is partially supported by the European Commission under the 7th framework programme EURECA Project, the Dutch national project COMMIT/Data2Semantics, the major international cooperation project No. 61420106005 funded by China National Foundation of Natural Science. The first author is funded by the China Scholarship Council.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Qing Hu
    • 1
    • 2
    Email author
  • Zhisheng Huang
    • 1
  • Annette ten Teije
    • 1
  • Frank van Harmelen
    • 1
  1. 1.Department of Computer ScienceVU University AmsterdamAmsterdamThe Netherlands
  2. 2.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina

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