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

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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).
Original languageEnglish
Title of host publicationKnowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers
EditorsDavid Riaño, Richard Lenz, Manfred Reichert
Place of PublicationCham
PublisherSpringer/Verlag
Pages120-132
Number of pages13
Volume10096 LNAI
ISBN (Print)9783319550138
DOIs
Publication statusPublished - 2017
EventHEC International Joint Workshop on Knowledge Representation for Health Care, KR4HC/ProHealth 2016 - Munich, Germany
Duration: 2 Sep 20162 Sep 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10096 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceHEC International Joint Workshop on Knowledge Representation for Health Care, KR4HC/ProHealth 2016
CountryGermany
CityMunich
Period2/09/162/09/16

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