Event

September 10-13, 2017 Congress Center, Basel, Switzerland

Basel Life Innovation Forums 2017

Innovating MedComms

George Garrity will be presenting on two topics during the Innovating MedComms panel: How to ensure content quality in a world of overwhelming scientific complexity, 1:30pm-2:30pm (Machine learning-based tools for peer review) and Scientific discovery In the Machine Age: New tools for competitive advantage, 3:30pm-4:30pm (Machine learning tools for discovering scientific content). Both sessions are in the Shanghai 1 room, and videos will be made available after the event.

The first session (Machine learning tools for discovering scientific content) will showcase how novel semantic tagging and document classification methods can be used to enrich content by unobtrusively integrating externally curated resources and references. Further discussion will explore how these curated resources can serve as hidden metrics that provide a supplementary measure regarding the significance of various research artifacts or concepts in a given field of study.

The following session focuses on applying machine learning tools to the peer review process.

George Garrity reasons that most people underestimate the amount of work that goes into the process. “The publisher distributes your content, they polish it, they make sure there’s an archival version, but they also provide all the necessary quality control, and this is typically done by peer review,” he said.

The peer review process is essential for checking that valid arguments and conclusions are present, with appropriate priority, provenance and originality. However, it can be costly and very time-consuming, thus there is great interest in automating as much of the process as possible.

Hoping to do just that, a suite of tools from NamesforLife allows processing of a raw manuscript in mere minutes, validating facts, structure, terminology and cited resources, and annotating any “red flags”. The automation can then extend to the peer review stage, cross-checking the intended submission with a pool of some 40,000 documents in order to identify candidate reviewers based on relevant publication records.

The process removes selection bias, screens for conflicts of interest, and tracks ongoing reviewer performance. What’s more, it keeps up-to-date contact information for reviewers, and constructs a compelling email to send to the reviewer to encourage their participation.

Peer Review and Machine Learning Adaptation (257.5MB YouTube) How Semantic Tools Drive Scientific Content Discovery (243.2MB YouTube)

[permalink] Posted September 10, 2017.