PT  - JOURNAL ARTICLE
AU  - Jon Brassey
AU  - Christopher Price
AU  - Jonny Edwards
AU  - Markus Zlabinger
AU  - Alexandros Bampoulidis
AU  - Allan Hanbury
TI  - Developing a fully automated evidence synthesis tool for identifying, assessing and collating the evidence
AID  - 10.1136/bmjebm-2018-111126
DP  - 2019 Aug 29
TA  - BMJ Evidence-Based Medicine
PG  - bmjebm-2018-111126
4099  - http://ebm.bmj.com/content/early/2019/08/29/bmjebm-2018-111126.short
4100  - http://ebm.bmj.com/content/early/2019/08/29/bmjebm-2018-111126.full
AB  - Evidence synthesis is a key element of evidence-based medicine. However, it is currently hampered by being labour intensive meaning that many trials are not incorporated into robust evidence syntheses and that many are out of date. To overcome this, a variety of techniques are being explored, including using automation technology. Here, we describe a fully automated evidence synthesis system for intervention studies, one that identifies all the relevant evidence, assesses the evidence for reliability and collates it to estimate the relative effectiveness of an intervention. Techniques used include machine learning, natural language processing and rule-based systems. Results are visualised using modern visualisation techniques. We believe this to be the first, publicly available, automated evidence synthesis system: an evidence mapping tool that synthesises evidence on the fly.