DocumentCode :
3714509
Title :
A prototype for a hybrid system to support systematic review teams: A case study of organ transplantation
Author :
Tanja Bekhuis;Eugene Tseytlin;Kevin J. Mitchell
Author_Institution :
Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, USA
fYear :
2015
Firstpage :
940
Lastpage :
947
Abstract :
We describe a prototype for a hybrid system designed to reduce the number of citations needed to re-screen (NNRS) by systematic reviewers, where citations include titles, abstracts, and metadata. The system obviates the need for screening the entire set of citations a second time, which is typically done to control human error. The reference set is based on a complex review about organ transplantation (N=10,796 citations). Data were split into 50% training and test sets, randomly stratified for percentage eligible citations. The system consists of a rule-based module and a machine-learning (ML) module. The former substantially reduces the number of negative citations passed to the ML module and improves imbalance. Relative to the baseline, the system reduces classification error (5.6% vs 2.9%) thereby reducing NNRS by 47.3% (300 vs 158). We discuss the implications of de-emphasizing sensitivity (recall) in favor of specificity and negative predictive value to reduce screening burden.
Keywords :
Systematics
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/BIBM.2015.7359810
Filename :
7359810
Link To Document :
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