In this special issue of PAIN Reports, guest-edited by Dr Georgios Baskozos, six articles from leaders in the field focus on “big pain data,” the large datasets and the associated methods for data analysis that are currently emerging in pain research. This collection of articles highlights the power and potential as well as points of caution that multidisciplinary research utilising big data and their associated methods and interpretations present for pain research. All articles are available in open access.
- "The effect of psychological factors on pain outcomes: lessons learned for the next generation of research" by Geert Crombez, Elke Veirman, Dimitri Van Ryckeghem, Whitney Scott, and Annick De Paepe | Link |see also: Psychosocial factors (WP3)
- "Big data, big consortia, and pain: UK Biobank, PAINSTORM, and DOLORisk" by Harry Hébert, Mathilde Pascal, Blair Smith, David Wynick, and David Bennett | Link | see also: Protocols and recruitment (WP1), Collaborations, DOLORisk
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"Personalizing digital pain management with adapted machine learning approach" by Yifat Fundoiano-Hershcovitz, Keren Pollak, and Pavel Goldstein | Link
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"Real-world data and evidence in pain research: a qualitative systematic review of methods in current practice" by Jan Vollert, et al. | Link | see also: Data modelling (WP7)
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"Artificial intelligence and machine learning in pain research: a data scientometric analysis" by Jörn Lötsch, Alfred Ultsch, Benjamin Mayer, and Dario Kringel | Link
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"The power of integrating data: advancing pain research using meta-analysis" by Joel Fundaun, Elizabeth Thomas, Annina Schmid, and Georgios Baskozos | Link | see also: Data modelling (WP7)
