Enhancing Systematic Review via the Application of Machine Learning to the “bibliOME”: A Collaboration with TenWise to Reveal Insights into ME/CFS

Using AI to conduct systematic reviews of the human & animal literature to uncover patterns that will support our understanding of ME/CFS physiology & pathology

schedule Date & time
Date/time
13 May 2021 1:00pm - 13 May 2021 1:30pm
person Speaker

Speakers

Associate Professor Brett Lidbury, National Centre for Epidemiology and Public Health
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Cost

Cost per person: 0.00

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Description

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Systematic Review (SR) is recognised as an essential step to ensure high quality research designs, generation of hypotheses, and ultimately, reliable results. With the constant expansion of the biomedical literature (the bibliOME), this can be an onerous process. In collaboration with TenWise (B.V.), The Netherlands, we are conducting an exploration of the human and animal literature to uncover patterns that will support our understanding of ME/CFS physiology and pathology.

You can read more about this collaboration and research in a recent news article.

About Brett

Brett Lidbury is an Associate Professor with NCEPH - RSPH at the Australian National University, Canberra. Brett is also a Visiting Associate Professor with the Department of Health Evidence, Radboud University Medical Centre, Nijmegen, The Netherlands. Ongoing research interests include virus - host interaction and pathogenesis, diagnostics and biomarker development, now pursued in silico through machine learning methods. Previous laboratory-based investigations on the pathogenesis of the Australian arbovirus, Ross River (RRV), a virus suspected in cases of post-viral fatigue syndromes and ME, ultimately lead to current programmes in ME/CFS research. Brett is a member of the ME/CFS Discovery Research Network (MDRN) and the consortium of Australian and UK scientists charged with establishing the first Australian ME Biobank and data repository.

Location

Bob Douglas Lecture Theatre, Building 62, Mills Road ACTON 2601

Zoom: https://anu.zoom.us/j/98923272351?pwd=V1paK1Nsazd2dDlUZmlQaElpeVVvUT09

Meeting ID: 989 2327 2351 | Password: 037507