Complementary approaches to searching MEDLINE may be sufficient for updating systematic reviews

Sampson, Margaret; Bruijn, Berry de; Urquhart, Christine; Shojania, Kaveh G

doi:10.1016/j.jclinepi.2016.03.004

Cited by 24 publications

(21 citation statements)

References 24 publications

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“…With upcoming automation processes and the increasing availability of validated search filters, it is conceivable that the cessation of health economic-specific databases will no longer be a misfortune for the scientific community. For the last decade, it seems that most research concerned with developing search strategies for detecting EEs focuses on the two major players, MEDLINE and Embase anyway [1,3,60,70,93,94]. In the near future, a search of those two databases could possibly be sufficient to detect most EEs.…”

Section: Expert Commentary and Five-year Viewmentioning

confidence: 99%

How to prepare a systematic review of economic evaluations for clinical practice guidelines: database selection and search strategy development (part 2/3)

Thielen

Mastrigt

Burgers

et al. 2016

Expert Review of Pharmacoeconomics & Outcomes Research

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This article is part of the series "How to prepare a systematic review of economic evaluations (EES) for informing evidence-based healthcare decisions", in which a five-step approach is proposed. Areas covered: This paper focuses on the selection of relevant databases and developing a search strategy for detecting EEs, as well as on how to perform the search and how to extract relevant data from retrieved records. Expert commentary: Thus far, little has been published on how to conduct systematic review EEs. Moreover, reliable sources of information, such as the Health Economic Evaluation Database, have ceased to publish updates. Researchers are thus left without authoritative guidance on how to conduct SR-EEs. Together with van Mastrigt et al. we seek to fill this gap.

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Section: Expert Commentary and Five-year Viewmentioning

confidence: 99%

How to prepare a systematic review of economic evaluations for clinical practice guidelines: database selection and search strategy development (part 2/3)

Thielen

Mastrigt

Burgers

et al. 2016

Expert Review of Pharmacoeconomics & Outcomes Research

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“…The second would be a search using the Related Articles feature with the PubMed IDs (PMIDs) of the three newest and three largest included studies as the seed articles. 5,6 Can searches for updates be limited update by date?…”

Section: Text Word Termsmentioning

confidence: 99%

Literature reviews: Patient-centred care

Yeung

2017

Br Dent J

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“…Introduction *Nowadays, the volume and growth rate of online biomedical literature creates new challenges for the researchers. MEDLINE (Sampson et al, 2016) is the primary source of medical literature, which consists of over 23 million online entries with a growth rate of over 800 thousand new citations every year. MEDLINE documents are manually categorized under 22,568 MeSH category names by experts from the National Library of Medicine (NLM).…”

Section: Introductionmentioning

confidence: 99%

Review of feature extraction approaches on biomedical text classification

al.¹

2020

Int. j. adv. appl. sci.

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The overcoming volume of online biomedical literature causes congestion of data and difficulties in organizing these documents and also to retrieve the required documents from the database, especially in the Medline database. One of the solutions to surpass the overwhelming of documents is to apply classification. However, each document must be represented by a set of terminology or feature vectors. The identification of terminology or feature from biomedical literature is one of the most important and challenging tasks in text classification. This is due to a large number of new features and entities that appear in the biomedical domain. In addition, combining sets of features from different terminological resources leads to naming conflicts such as homonymous use of names and terminological ambiguities. Therefore, the purpose of this research is to investigate and evaluate the effective ways for extracting the relevant and meaningful features in order to increase the classification accuracy and improve the performance of web searches. Towards this effort, we conduct several classification experiments to evaluate and compare the effectiveness of feature extraction approaches for extracting the relevant and informative features from the biomedical literature. For our experiments, we use two different sets of features, which are a set of features that are extracted using the Genia tagger tool and set of features that are extracted by medical experts from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM). The results show the performance of classification using features that are extracted by medical experts outperform the performance of classification using the Genia Tagger tool when applying feature selection method.

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Complementary approaches to searching MEDLINE may be sufficient for updating systematic reviews

Cited by 24 publications

References 24 publications

How to prepare a systematic review of economic evaluations for clinical practice guidelines: database selection and search strategy development (part 2/3)

How to prepare a systematic review of economic evaluations for clinical practice guidelines: database selection and search strategy development (part 2/3)

Literature reviews: Patient-centred care

Review of feature extraction approaches on biomedical text classification

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