Training sample selection: Impact on screening automation in diagnostic test accuracy reviews

Research output: Contribution to journalArticleAcademicpeer-review

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When performing a systematic review, researchers screen the articles retrieved after a broad search strategy one by one, which is time-consuming. Computerised support of this screening process has been applied with varying success. This is partly due to the dependency on large amounts of data to develop models that predict inclusion. In this paper, we present an approach to choose which data to use in model training and compare it with established approaches. We used a dataset of 50 Cochrane diagnostic test accuracy reviews, and each was used as a target review. From the remaining 49 reviews, we selected those that most closely resembled the target review's clinical topic using the cosine similarity metric. Included and excluded studies from these selected reviews were then used to develop our prediction models. The performance of models trained on the selected reviews was compared against models trained on studies from all available reviews. The prediction models performed best with a larger number of reviews in the training set and on target reviews that had a research subject similar to other reviews in the dataset. Our approach using cosine similarity may reduce computational costs for model training and the duration of the screening process.
Original languageEnglish
Pages (from-to)831-841
Number of pages11
JournalResearch synthesis methods
Issue number6
Early online date2021
Publication statusPublished - Nov 2021


  • computerised support
  • cosine similarity
  • machine learning
  • screening automation
  • training sample selection

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