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Table 1 Examples of machine learning systems available for use in systematic reviews

From: Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

  Example tools Comments
Search—finding RCTs RobotSearch (
Cochrane Register of Studies (
RCT tagger (
• Validated machine learning filters available for identifying RCTs and suitable for fully automatic use
• Conventional topic-specific keyword search strategy still needed
• No widely available tools for non-RCT design currently
Search—literature exploration Thalia ( Allows search of PubMed for concepts (i.e. chemicals, diseases, drugs, genes, metabolites, proteins, species and anatomical entities)
Screening Abstrackr ( [30]
EPPI reviewer ( [31]
RobotAnalyst ( [32]
SWIFT-Review (
Colandr (
Rayyan (
• Screening systems automatically sort a search retrieval by relevance
• RobotAnalyst and SWIFT-Review also allow topic modelling, where abstracts relating to similar topics are automatically grouped, allowing the user to explore the search retrieval.
Data extraction ExaCT (
RobotReviewer (
NaCTeM text mining tools for automatically extracting concepts relating to genes and proteins (NEMine), yeast metabolites (Yeast MetaboliNER), and anatomical entities (AnatomyTagger) (
• These prototype systems automatically extract data elements (e.g. sample sizes, descriptions of PICO elements) from free-texts.
Bias assessment RobotReviewer ( • Automatic assessment of biases in reports of RCTs
• System recommended for semi-automatic use (i.e. with human reviewer checking and correcting the ML suggestions)