Trip Database Blog

I recently gave an update on the progress of the system (see Automated rapid reviews).  In this I highlight the variables we’ll be able to automatically assess for each paper (RCT or systematic review):

• P – population/disease
• I – intervention
• C – comparison (if there is one)
• Sentiment – does the trial favour the intervention or not
• Sample size – is this a large or small trial
• Risk of Bias – via RobotReviewer, which is already on the site

As our systems processes all the articles we have to figure out how to create an output.  The outline brief I’ve suggested to our designers is:

Clearly I’m no designer! But I hope you get the picture!  The design works on two levels:

• Top level – for a given condition each ‘blob’ will consist of a single intervention.  Size of blob will indicate the size of the evidence (based on sample size of trials), horizontal axis will represent the date of the first trial for that particular intervention, while the vertical axis will indicate likely effectiveness,
• Second level – If a user clicks on a blob in the top level, this will be unpacked to break down each intervention in to the component trials.  Again, using similar plotting methods (sample size = size of blob, date of individual trial of horizontal access and effectiveness on the vertical).

It will look nicer and we’re exploring other visualisation techniques such as this one.

This needs to be ready by the end of September, so just over three weeks!

As part of the KConnect work (EU funded Horizon 2020 project) we have been doing a fair bit of work exploring the automatic extraction of various elements from RCTs and systematic reviews.  If we can automatically understand what a paper is about it can open up all sorts of avenues with regard search and evidence synthesis.

The KConnect output is virtually ready for Trip to use and it will allow us (with decent, but not perfect accuracy) the following elements from a RCT or systematic review:

• P – population/disease
• I – intervention
• C – comparison (if there is one)
• Sentiment – does the trial favour the intervention or not
• Sample size – is this a large or small trial
• Risk of Bias – via RobotReviewer, which is already on the site (see this post)

So, what can we do with this?  A few examples:

• For a given condition we can identify all the trials in this area and what the interventions are.
• We can rank the interventions on likely effectiveness
• For a given intervention we can look at what conditions it’s been used it.
• We could present graphic like Information is Beautiful’s Snake Oil for a given condition and/or intervention.
• We can massively increase the coverage of our Answer Engine.

Also, all this will be fully automatic, as new trials are added to Trip they will get processed and added to the system.

We’ve got a few technical issues to go (integrating the various systems) but we are so close. You will have no idea how long I’ve fantasised about the system.  And, even though it won’t be perfect, it should stand as a very good proof of concept.