A REVIEW ON USE OF AUTOMATION IN SYSTEMATIC REVIEWS FOR SCIENTIFIC EVIDENCE GENERATION
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 11, Pages 8584-8603
AbstractBackground: Systematic reviews are primarily literature reviews performed using systematic methods. A well-conducted review enables clinicians and policy-makers to stay updated in their respective fields of interest, and make informed decisions. Once fully automated, it will enable researchers to conduct systematic reviews efficiently, produce high-quality evidence, and contribute more to the field of evidence-based medicine. Mathematical models based on results from swiftly conducted systematic reviews may predict the future incidence or outbreak scenarios for diseases, which are public health problems.
Main text: This paper presents an exhaustive literature review on the common methods that can be deployed for automating sub-processes with-in a systematic review, their scope, current use, and limitations. A comprehensive search in PubMed and Google Scholar to identify articles or reviews describing use of existing automation tools within the systematic review process was performed. The main methods discussed include machine learning or artificial intelligence, text-mining, and text classification. Current gaps as well as opportunities to improve the quality of a systematic review and the overall evidence generation process are also reviewed.
Conclusions: Several technologies like Automatic Term Recognition (ATR), text-mining, text identification, as well as machine learning have already been incorporated to the general process of systematic reviews and so are common tools like Abstrackr, DistillerSR, and RobotAnalyst. The use of automatic classifiers, supervised classification algorithms, and natural language processing has been seen for search of pertinent literature. Harmonization of the existing tools is imperative for further development and quality evidence generation.
Keywords: Automation, Evidence-based Medicine, Machine Learning, Text-mining
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