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).
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
StatePublished - 2017
EventHEC International Joint Workshop on Knowledge Representation for Health Care, KR4HC/ProHealth 2016 - Munich, Germany

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

Cite this

Hu, Q., Huang, Z., ten Teije, A., & van Harmelen, F. (2017). Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering. In D. Riaño, R. Lenz, & M. Reichert (Eds.), Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers (Vol. 10096 LNAI, pp. 120-132). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10096 LNAI). Cham: Springer/Verlag. DOI: 10.1007/978-3-319-55014-5_8

Hu, Qing; Huang, Zisheng; ten Teije, Annette; van Harmelen, Frank / Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering.

Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers. ed. / David Riaño; Richard Lenz; Manfred Reichert. Vol. 10096 LNAI Cham : Springer/Verlag, 2017. p. 120-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10096 LNAI).

Research output: Scientific - peer-reviewConference contribution

@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",

}

Hu, Q, Huang, Z, ten Teije, A & van Harmelen, F 2017, Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering. in D Riaño, R Lenz & M Reichert (eds), Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers. vol. 10096 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10096 LNAI, Springer/Verlag, Cham, pp. 120-132, HEC International Joint Workshop on Knowledge Representation for Health Care, KR4HC/ProHealth 2016, Munich, Germany, 2-2 September. DOI: 10.1007/978-3-319-55014-5_8

Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering. / Hu, Qing; Huang, Zisheng; ten Teije, Annette; van Harmelen, Frank.

Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers. ed. / David Riaño; Richard Lenz; Manfred Reichert. Vol. 10096 LNAI Cham : Springer/Verlag, 2017. p. 120-132 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10096 LNAI).

Research output: Scientific - peer-reviewConference contribution

TY - CHAP

T1 - Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering

AU - Hu,Qing

AU - Huang,Zisheng

AU - ten Teije,Annette

AU - van Harmelen,Frank

PY - 2017

Y1 - 2017

N2 - 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).

AB - 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).

UR - http://www.scopus.com/inward/record.url?scp=85014916085&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014916085&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-55014-5_8

DO - 10.1007/978-3-319-55014-5_8

M3 - Conference contribution

SN - 9783319550138

VL - 10096 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 120

EP - 132

BT - Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers

PB - Springer/Verlag

ER -

Hu Q, Huang Z, ten Teije A, van Harmelen F. Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering. In Riaño D, Lenz R, Reichert M, editors, Knowledge Representation for Health Care: HEC 2016 International Joint Workshop, KR4HC/ProHealth 2016, Munich, Germany, September 2, 2016, Revised Selected Papers. Vol. 10096 LNAI. Cham: Springer/Verlag. 2017. p. 120-132. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-55014-5_8