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Statistical stopping criteria for automated screening in systematic reviews
2022
Active learning for systematic review screening promises to reduce the human effort required to identify relevant documents for a systematic review. Machines and humans work together, with humans providing training data, and the machine optimising the documents that the humans screen. This enables the identification of all relevant documents after viewing only a fraction of the total documents. However, current approaches lack robust stopping criteria, so that reviewers do not know when they
doi:10.34657/9283
fatcat:rj3yqhoy7bag5egjepbryuws2q