Treffer: Trial2rev: Combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews.

Title:
Trial2rev: Combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews.
Authors:
Martin P; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia., Surian D; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia., Bashir R; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia., Bourgeois FT; Computational Health Informatics Program, Children's Hospital Boston, Boston, Massachusetts, USA.; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA., Dunn AG; Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Source:
JAMIA open [JAMIA Open] 2019 Jan 11; Vol. 2 (1), pp. 15-22. Date of Electronic Publication: 2019 Jan 11 (Print Publication: 2019).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press on behalf of the American Medical Informatics Association Country of Publication: United States NLM ID: 101730643 Publication Model: eCollection Cited Medium: Internet ISSN: 2574-2531 (Electronic) Linking ISSN: 25742531 NLM ISO Abbreviation: JAMIA Open Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: [Cary, NC] : Oxford University Press on behalf of the American Medical Informatics Association, [2018]-
References:
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Contributed Indexing:
Keywords: bibliographic databases; databases as topic; review literature as topic; semi-supervised learning
Entry Date(s):
Date Created: 20200128 Latest Revision: 20220412
Update Code:
20250114
PubMed Central ID:
PMC6951914
DOI:
10.1093/jamiaopen/ooy062
PMID:
31984340
Database:
MEDLINE

Weitere Informationen

Objectives: Systematic reviews of clinical trials could be updated faster by automatically monitoring relevant trials as they are registered, completed, and reported. Our aim was to provide a public interface to a database of curated links between systematic reviews and trial registrations.
Materials and Methods: We developed the server-side system components in Python, connected them to a PostgreSQL database, and implemented the web-based user interface using Javascript, HTML, and CSS. All code is available on GitHub under an open source MIT license and registered users can access and download all available data.
Results: The trial2rev system is a web-based interface to a database that collates and augments information from multiple sources including bibliographic databases, the ClinicalTrials.gov registry, and the actions of registered users. Users interact with the system by browsing, searching, or adding systematic reviews, verifying links to trials included in the review, and adding or voting on trials that they would expect to include in an update of the systematic review. The system can trigger the actions of software agents that add or vote on included and relevant trials, in response to user interactions or by scheduling updates from external resources.
Discussion and Conclusion: We designed a publicly-accessible resource to help systematic reviewers make decisions about systematic review updates. Where previous approaches have sought to reactively filter published reports of trials for inclusion in systematic reviews, our approach is to proactively monitor for relevant trials as they are registered and completed.
(© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association.)