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A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates

Overview of attention for article published in Journal of Biomedical Informatics, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

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Title
A shared latent space matrix factorisation method for recommending new trial evidence for systematic review updates
Published in
Journal of Biomedical Informatics, March 2018
DOI 10.1016/j.jbi.2018.01.008
Pubmed ID
Authors

Didi Surian, Adam G. Dunn, Liat Orenstein, Rabia Bashir, Enrico Coiera, Florence T. Bourgeois

Abstract

Clinical trial registries can be used to monitor the production of trial evidence and signal when systematic reviews become out of date. However, this use has been limited to date due to the extensive manual review required to search for and screen relevant trial registrations. Our aim was to evaluate a new method that could partially automate the identification of trial registrations that may be relevant for systematic review updates. We identified 179 systematic reviews of drug interventions for type 2 diabetes, which included 537 clinical trials that had registrations in ClinicalTrials.gov. Text from the trial registrations were used as features directly, or transformed using Latent Dirichlet Allocation (LDA) or Principal Component Analysis (PCA). We tested a novel matrix factorisation approach that uses a shared latent space to learn how to rank relevant trial registrations for each systematic review, comparing the performance to document similarity to rank relevant trial registrations. The two approaches were tested on a holdout set of the newest trials from the set of type 2 diabetes systematic reviews and an unseen set of 141 clinical trial registrations from 17 updated systematic reviews published in the Cochrane Database of Systematic Reviews. The performance was measured by the number of relevant registrations found after examining 100 candidates (recall@100) and the median rank of relevant registrations in the ranked candidate lists. The matrix factorisation approach outperformed the document similarity approach with a median rank of 59 (of 128,392 candidate registrations in ClinicalTrials.gov) and recall@100 of 60.9% using LDA feature representation, compared to a median rank of 138 and recall@100 of 42.8% in the document similarity baseline. In the second set of systematic reviews and their updates, the highest performing approach used document similarity and gave a median rank of 67 (recall@100 of 62.9%). A shared latent space matrix factorisation method was useful for ranking trial registrations to reduce the manual workload associated with finding relevant trials for systematic review updates. The results suggest that the approach could be used as part of a semi-automated pipeline for monitoring potentially new evidence for inclusion in a review update.

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Unknown 10 100%
Readers by discipline Count As %
Unknown 10 100%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 March 2019.
All research outputs
#1,453,479
of 12,635,028 outputs
Outputs from Journal of Biomedical Informatics
#80
of 1,177 outputs
Outputs of similar age
#48,634
of 268,834 outputs
Outputs of similar age from Journal of Biomedical Informatics
#4
of 37 outputs
Altmetric has tracked 12,635,028 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,177 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,834 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.