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being labour intensive meaning that many trials are not incorporated into robust evidence syntheses and that many are out of date.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.
- health informatics
- world wide web technology
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Contributors JB and CP conceived and designed the work. MZ developed the algorithms for PIC extraction and conducted the evaluation. AB developed the sample size methods. AH led the overall grant helped develop the overview methods for the work and oversaw the work of MZ and AB. JE developed the sentiment analysis system. All authors contributed to the drafting and completion of the paper.
Funding European Commission > Horizon 2020 Framework Programme No 644753 (KConnect).
Competing interests Both JB and CP are shareholders in Trip Database. JB is also a member of the editorial board of BMJ EBM. No other declared competing interests from the other authors.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
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