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

Paige Martin, Didi Surian, Rabia Bashir, Florence T. Bourgeois, Adam G. Dunn

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.
LanguageEnglish
Article numberooy062
Pages15-22
Number of pages8
JournalJAMIA Open
Volume2
Issue number1
Early online date11 Jan 2019
DOIs
Publication statusPublished - Apr 2019

Fingerprint

Crowdsourcing
Databases
Boidae
Bibliographic Databases
Politics
Licensure
Registries
Software
Clinical Trials
Machine Learning

Bibliographical note

Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • review literature as topic
  • semi-supervised learning
  • databases as topic
  • bibliographic databases

Cite this

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title = "Trial2rev: combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews",
abstract = "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.",
keywords = "review literature as topic, semi-supervised learning, databases as topic, bibliographic databases",
author = "Paige Martin and Didi Surian and Rabia Bashir and Bourgeois, {Florence T.} and Dunn, {Adam G.}",
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Trial2rev : combining machine learning and crowd-sourcing to create a shared space for updating systematic reviews. / Martin, Paige; Surian, Didi; Bashir, Rabia; Bourgeois, Florence T.; Dunn, Adam G.

In: JAMIA Open, Vol. 2, No. 1, ooy062, 04.2019, p. 15-22.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Trial2rev

T2 - JAMIA Open

AU - Martin, Paige

AU - Surian, Didi

AU - Bashir, Rabia

AU - Bourgeois, Florence T.

AU - Dunn, Adam G.

N1 - Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2019/4

Y1 - 2019/4

N2 - 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.

AB - 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.

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