Machine learning assisted citation screening for systematic reviews

Dhrangadhariya, Anjani (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Hilfiker, Roger (School of Health Sciences, HES-SO Valais-Wallis, Leukerbad, Switzerland) ; Schaer, Roger (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva (UNIGE), Geneva, Switzerland)

Evidence-based practice is highly dependent upon up-to-date systematic reviews (SR) for decision making. However, conducting and updating systematic reviews, especially the citation screening for identification of relevant studies, requires much human work and is therefore expensive. Automating citation screening using machine learning (ML) based approaches can reduce cost and labor. Machine learning has been applied to automate citation screening but not for the SRs with very narrow research questions. This paper reports the results and observations for an ongoing research that aims to automate citation screening for SRs with narrow research questions using machine learning. The research also sheds light on the problem of class imbalance and class overlap on the performance of ML classifiers when applied to SRs with narrow research questions.


Keywords:
Faculty:
Economie et Services
Santé
School:
HEdS-VS
HEG-VS
Institute:
Institut Informatique de gestion
Institut Santé
Subject(s):
Economie/gestion
Publisher:
Amsterdam, The Netherlands, OIS Press
Date:
2020-06
Amsterdam, The Netherlands
OIS Press
Pagination:
5 p.
Published in:
Digital personalized health and medicine
Series Statement:
Studies in Health Technology and Informatics, vol. 270
Author of the book:
Pape-Haugaard, Louise B. ; Aalborg University, Denmark
DOI:
Appears in Collection:



 Record created 2020-09-15, last modified 2020-10-27

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