Toward modernizing the systematic review pipeline in genetics: efficient 
updating via data mining

Wallace, B. C.; Small, K.; Brodley, C. E.; Lau, J.; Schmid, C. H.; Bertram, L.; Lill, C. M.; Cohen, J. T.; Trikalinos, T. A.

doi:10.1038/gim.2012.7

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

Toward modernizing the systematic review pipeline in genetics: efficient updating via data mining

MPS-Authors

/persons/resource/persons50098

Bertram, L.
Neuropsychiatric Genetics (Lars Bertram), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;

/persons/resource/persons50411

Lill, C. M.
Neuropsychiatric Genetics (Lars Bertram), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society;
Department of Neurology, University Medical Center of the Johannes Gutenberg-University;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

Wallace.pdf
(Publisher version), 364KB

Supplementary Material (public)

There is no public supplementary material available

Citation

Wallace, B. C., Small, K., Brodley, C. E., Lau, J., Schmid, C. H., Bertram, L., et al. (2012). Toward modernizing the systematic review pipeline in genetics: efficient updating via data mining. GENETICS IN MEDICINE, 14(7), 663-669. doi:10.1038/gim.2012.7.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000E-F0B5-4

Abstract

PURPOSE: The aim of this study was to demonstrate that modern data mining tools can be used as one step in reducing the labor necessary to produce and maintain systematic reviews. METHODS: We used four continuously updated, manually curated resources that summarize MEDLINE-indexed articles in entire fields using systematic review methods (PDGene, AlzGene, and SzGene for genetic determinants of Parkinson disease, Alzheimer disease, and schizophrenia, respectively; and the Tufts Cost-Effectiveness Analysis (CEA) Registry for cost-effectiveness analyses). In each data set, we trained a classification model on citations screened up until 2009. We then evaluated the ability of the model to classify citations published in 2010 as "relevant" or "irrelevant" using human screening as the gold standard. RESULTS: Classification models did not miss any of the 104, 65, and 179 eligible citations in PDGene, AlzGene, and SzGene, respectively, and missed only 1 of 79 in the CEA Registry (100% sensitivity for the first three and 99% for the fourth). The respective specificities were 90, 93, 90, and 73%. Had the semiautomated system been used in 2010, a human would have needed to read only 605/5,616 citations to update the PDGene registry (11%) and 555/7,298 (8%), 717/5,381 (13%), and 334/1,015 (33%) for the other three databases. CONCLUSION: Data mining methodologies can reduce the burden of updating systematic reviews, without missing more papers than humans.