ME/CFS Science Blog
Senior Member (Voting Rights)
I doubt that AI could do this, what I have seen is mostly people using existing PubMed tags or things mentioned in the abstract.I imagine that adding tags is doable between database, application layer and user interface, and that the hard part would be adding sensible tags to all these papers. Potentially that could also be crowd-sourced, but I wouldn't be surprised if AI could make a decent first pass.
With a team it should be doable to screen all ME/CFS papers on PubMed: there are approximately 7700 at the moment. Many are short commentaries or letters. I (and many others on the forum here) have probably read most of them already.
Valuable things to extract are:
- The type of publication (letter/commentary, review, experiment, prospective study, randomized trial, etc)
- The number of ME/CFS participants in the trial or experiment
- The area of research (genetics, metabolomics, immune system, exercise testing, Chinese medicine etc.)
- The methods used (cytokine measurement, RNA-seq, CPET, which questionnaires were used, etc)
One problem is that it probably needs to be updated for new papers that come out, which would make it an ongoing effort that never stops.