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Training sample selection: Impact on screening automation in diagnostic test accuracy reviews
Author(s) -
Altena Allard J.,
Spijker René,
Leeflang Mariska M. G.,
Olabarriaga Sílvia Delgado
Publication year - 2021
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.1518
Subject(s) - computer science , metric (unit) , machine learning , artificial intelligence , cosine similarity , set (abstract data type) , data mining , systematic review , similarity (geometry) , sample size determination , process (computing) , statistics , pattern recognition (psychology) , medline , mathematics , political science , law , economics , image (mathematics) , programming language , operating system , operations management
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.