UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Identifying diagnostic test accuracy publications using a deep model

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. Green open access

[thumbnail of Singh et al. - 2017 - Identifying Diagnostic Test Accuracy Publications using a Deep Model.pdf]
Preview
Text
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
Downloads since deposit
26Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item