Advertisement: EBCam mid banner
Advertisement: Mogrify
Advertisement: Simpsons Creative
ARM Innovation Hub
Advertisement: Wild Knight Vodka
Advertisement: TTP
Mid banner advertisement: BDO
RealVNC mid-banner general
Barr Ellison Solicitors – commercial property
Advertisement: EY Mid banner
Advertisement: RSM
Advertisement: Cambridge Network
RealVNC mid banner careers
22 April, 2020 - 13:07 By Kate Sweeney

Judge alumnus’ AI-ML tool maps scientific evidence of COVID-19

Knowledge sharing is an essential element of Cambridge Judge Business School (CJBS) and scientific knowledge sharing about COVID-19 lies behind the Evidence Navigator – a tool that Cambridge Judge alumnus, Torsten Oliver Salge helped develop with colleagues at RWTH Aachen University in Germany.

Salge says the COVID-19 crisis has fuelled unprecedented efforts in science, industry and broader society to build new knowledge on how to contain the virus and its health, social and economic impact. 

As for medical research alone, more than 200 new journal articles on COVID-19 are published each day. While Salge says this is very encouraging, the sheer volume is likely to overwhelm the absorptive capacity of most. This is a major obstacle to the effective translation of scientific insights into evidence-based decision-making at all levels from individual researchers to national health policy, he says.

“In our opinion, what matters most for building a global learning community for COVID-19 is not only the creation and unconstrained sharing of knowledge on COVID-19, but also its curation at scale. 

“So we sought to showcase that machine learning techniques can offer valuable support in knowledge curation, as they are able to process large volumes of text in near real-time. As such, they can be a meaningful complement to the well-established manual curation of evidence.” 

The tool has not been designed primarily for frontline clinical staff that do their utmost to help as many patients as possible overcome COVID-19 often under highly challenging conditions. 

It is rather meant to support those involved in research activities on COVID-19 and those interested in connecting more closely with this evidence base – be it from the media, management or policy.  

Salge adds: “Most importantly, however, we wanted to demonstrate that machine learning and artificial intelligence have an important role to play in curating knowledge on COVID-19 – but also beyond. 

“Despite many efforts fuelled by the COVID-19 crisis, this potential remains to be fully exploited. Our hope is hence that the current crisis will help to turn machine learning and AI into partners for knowledge curation and translation,” he says.

The Evidence Navigator was informed by two long-standing streams of research in Salge’s Institute and emerged at the intersection of both. He says: “The first goes back to my own PhD at CJBS and centres on innovation and knowledge sharing in healthcare. 

“The second was initiated by David Antons, Professor in our Institute, and deals with text mining and bibliometric techniques as means of knowledge curation in science. We applied these techniques to map the knowledge landscape of entire journals, and we are now in the process of synthesising these insights.”

The Evidence Navigator combines bibliometric and text mining techniques to enable users to structure, synthesise and navigate the rapidly growing scientific evidence base on COVID-19. 

Salge’s team invites allcomers to use the Evidence Navigator and extract their own insights. The Evidence Navigator is freely available online at gruenwald.shinyapps.io/covid19-evi/

• PHOTOGRAPH: Cambridge Judge alumnus, Torsten Oliver Salge (right). Image courtesy – RWTH Aachen University.

Newsletter Subscription

Stay informed of the latest news and features