Improving reference prioritisation with PICO recognition

Austin J. Brockmeier · Meizhi Ju · Piotr Przybyła · Sophia Ananiadou

62 citations0/0 files storedBMC Medical Informatics and Decision Making

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Meta-analysis and systematic reviews Information Retrieval and Search Behavior Biomedical Text Mining and Ontologies

Abstract

BACKGROUND: Machine learning can assist with multiple tasks during systematic reviews to facilitate the rapid retrieval of relevant references during screening and to identify and extract information relevant to the study characteristics, which include the PICO elements of patient/population, intervention, comparator, and outcomes. The latter requires techniques for identifying and categorising fragments of text, known as named entity recognition. METHODS: A publicly available corpus of PICO annotations on biomedical abstracts is used to train a named entity recognition model, which is implemented as a recurrent neural network. This model is then applied to a separate collection of abstracts for references from systematic reviews within biomedical and health domains. The occurrences of words tagged in the context of specific PICO contexts are used as additional features for a relevancy classification model. Simulations of the machine learning-assisted screening are used to evaluate the work saved by the relevancy model with and without the PICO features. Chi-squared and statistical significance of positive predicted values are used to identify words that are more indicative of relevancy within PICO contexts. RESULTS: Inclusion of PICO features improves the performance metric on 15 of the 20 collections, with substantial gains on certain systematic reviews. Examples of words whose PICO context are more precise can explain this increase. CONCLUSIONS: Words within PICO tagged segments in abstracts are predictive features for determining inclusion. Combining PICO annotation model into the relevancy classification pipeline is a promising approach. The annotations may be useful on their own to aid users in pinpointing necessary information for data extraction, or to facilitate semantic search.

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References

2012Practical Bayesian Optimization of Machine Learning Algorithms 2015Using text mining for study identification in systematic reviews: a systematic review of current approaches 2012Deploying an interactive machine learning system in an evidence-based practice center 2014Systematic review automation technologies 2008Selecting Studies and Collecting Data 2018Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide 2005Reducing Workload in Systematic Review Preparation Using Automated Citation Classification 2015Erratum to: Using text mining for study identification in systematic reviews: a systematic review of current approaches

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