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      Information retrieval methodology for aiding scientific database search

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      Soft Computing
      Springer Science and Business Media LLC

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          Is Open Access

          Systematic review automation technologies

          Systematic reviews, a cornerstone of evidence-based medicine, are not produced quickly enough to support clinical practice. The cost of production, availability of the requisite expertise and timeliness are often quoted as major contributors for the delay. This detailed survey of the state of the art of information systems designed to support or automate individual tasks in the systematic review, and in particular systematic reviews of randomized controlled clinical trials, reveals trends that see the convergence of several parallel research projects. We surveyed literature describing informatics systems that support or automate the processes of systematic review or each of the tasks of the systematic review. Several projects focus on automating, simplifying and/or streamlining specific tasks of the systematic review. Some tasks are already fully automated while others are still largely manual. In this review, we describe each task and the effect that its automation would have on the entire systematic review process, summarize the existing information system support for each task, and highlight where further research is needed for realizing automation for the task. Integration of the systems that automate systematic review tasks may lead to a revised systematic review workflow. We envisage the optimized workflow will lead to system in which each systematic review is described as a computer program that automatically retrieves relevant trials, appraises them, extracts and synthesizes data, evaluates the risk of bias, performs meta-analysis calculations, and produces a report in real time.
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            Machine Learning With Big Data: Challenges and Approaches

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              Challenges and opportunities with big data

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                Author and article information

                Journal
                Soft Computing
                Soft Comput
                Springer Science and Business Media LLC
                1432-7643
                1433-7479
                April 2020
                October 12 2018
                April 2020
                : 24
                : 8
                : 5551-5560
                Article
                10.1007/s00500-018-3568-0
                a06aaee5-0563-4cb3-892f-0f72e70e392d
                © 2020

                http://www.springer.com/tdm

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