EPPI-Reviewer contains a number of automation tools -:
- Deduplication (not glamorous, but important!)
- Automatic clustering of studies
- 'Priority screening' for screening efficiency
- Five study type classifiers (RCT, systematic review, economic evaluation, COVID-19 categories, and Long COVID)
- 'Robots': the RobotReviewer, and Human Behaviour Change Project tools
- Automated review updates using Microsoft Academic
Many of the tools are extremely useful and can save valuable time. Some should also be considered as being under development and tested carefully before you use them in a review.
Machine Learning, Priority Screening and Classifiers
We have six pre-built machine learning classifiers in EPPI-Reviewer (with more in development). They are:
A specific guide to the Machine Learning facilities available within ER Web is available here. A separate document on Priority Screening can be found here.
During 2020 and 2021 we used the Microsoft Academic dataset to keep reviews and maps up to date automatically. We have now moved this facility over to use the OpenAlex dataset from its end-January 2022 update. We have a recent presentation on using OpenAlex available here.
Our introduction to using OpenAlex in EPPI-Reviewer is available here, with further information available here. (These will shortly be updated to reflect recent changes made in the user interface and move from MAG to OA.)
The 'Robots' in EPPI-Reviewer
EPPI-Reviewer enables you to use one automatic classification tools to characterise the research included in documents uploaded into the system (with one under development). You can access RobotReviewer in the same area as you upload documents to an individual record. Clicking 'Robot' will open a dialog box where you can select the robot you want to use, and the coding tool where you want to place the results.
For the RobotReviewer robot, you need to have copied the 'RobotReviewer' coding tool into your review. (To use the Human Behaviour Change robot, when available, you need to have brought the 'Human Behaviour Change Project prioritised codes' into your review.)
RobotReviewer can characterise important aspects of randomised trials of health interventions. You may find it particularly useful for assessing the study's risk of bias. It also identifies the 'PICO' characteristics of the study. For further information about RobotReviewer, please visit the RobotReviewer website.
The Human Behaviour Change robot is still under development, and this early version is currently being evaluated. It can also characterise randomised trials - most accurately in the field of smoking cessation. It is particularly focused on identifying the specific approaches to behaviour change that are used in the interventions evaluated in randomised trials. For futher information about this robot, please see the project website, and its growing set of papers on Wellcome Open Research.