. Hu, Q., Huang, Z., ten Teije , A., & van Harmelen , F. Volume 10096 LNAI, Riaño, D., Lenz, R., & Reichert, M., editors. Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering, pages 120–132. Springer/Verlag, 2017.
doi  abstract   bibtex   
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).
@inbook{f0a401b2137b45e5b2d32d0149c3160a,
  title     = "Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering",
  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).",
  author    = "Qing Hu and Zisheng Huang and {ten Teije}, Annette and {van Harmelen}, Frank",
  year      = "2017",
  doi       = "10.1007/978-3-319-55014-5_8",
  isbn      = "9783319550138",
  volume    = "10096 LNAI",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "120--132",
  editor    = "David Riaño and Richard Lenz and Manfred Reichert",
  booktitle = "Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers",
}
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