English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A systematic review identifies and collates various clinical studies andcompares data elements and results in order to provide an evidence based answerfor a particular clinical question. The process is manual and involves lot oftime. A tool to automate this process is lacking. The aim of this work is todevelop a framework using natural language processing and machine learning tobuild information extraction algorithms to identify data elements in a newprimary publication, without having to go through the expensive task of manualannotation to build gold standards for each data element type. The system isdeveloped in two stages. Initially, it uses information contained in existingsystematic reviews to identify the sentences from the PDF files of the includedreferences that contain specific data elements of interest using a modifiedJaccard similarity measure. These sentences have been treated as labeled data.ASupport Vector Machine (SVM) classifier is trained on this labeled data toextract data elements of interests from a new article. We conducted experimentson Cochrane Database systematic reviews related to congestive heart failureusing inclusion criteria as an example data element. The empirical results showthat the proposed system automatically identifies sentences containing the dataelement of interest with a high recall (93.75%) and reasonable precision(27.05% - which means the reviewers have to read only 3.7 sentences onaverage). The empirical results suggest that the tool is retrieving valuableinformation from the reference articles, even when it is time-consuming toidentify them manually. Thus we hope that the tool will be useful for automaticdata extraction from biomedical research publications. The future scope of thiswork is to generalize this information framework for all types of systematicreviews.

A Novel Framework to Expedite Systematic Reviews by Automatically Building Information Extraction Training Corpora