Singh, G;
Marshall, I;
Thomas, J;
Wallace, B;
(2017)
Identifying diagnostic test accuracy publications using a deep model.
In: Cappellato, L and Ferro, N and Goeuriot, L and Mandl, T, (eds.)
CLEF 2017 Working Notes: Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum.
CEUR Workshop Proceedings: Dublin, Ireland.
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Singh et al. - 2017 - Identifying Diagnostic Test Accuracy Publications using a Deep Model.pdf - Accepted Version Download (388kB) | Preview |
Abstract
In this work, we used a deep model architecture to identify DTA studies pertaining to a given review topic. We were provided the list of relevant documents selected based on abstracts and full text for different reviews topics. We extracted the abstract and title to be used as features to describe those documents, and learned the deep neural net model that takes as input the abstract and title of the studies, and topic of the review to obtain a binary classification of whether that study is a relevant DTA to the review in question.
| Type: | Proceedings paper |
|---|---|
| Title: | Identifying diagnostic test accuracy publications using a deep model |
| Event: | CLEF 2017 - Conference and Labs of the Evaluation Forum |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://ceur-ws.org/Vol-1866/ |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10058461 |
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