title: Toward modernizing the systematic review pipeline in genetics: efficient updating via data mining.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  Data mining methodologies can reduce the burden of updating systematic reviews, without missing more papers than humans.
authors.affiliation: Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA. byron.wallace@gmail.com
authors.forename: Byron C
authors.initials: BC
authors.lastname: Wallace
authors.forename: Kevin
authors.initials: K
authors.lastname: Small
authors.forename: Carla E
authors.initials: CE
authors.lastname: Brodley
authors.forename: Joseph
authors.initials: J
authors.lastname: Lau
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Schmid
authors.forename: Lars
authors.initials: L
authors.lastname: Bertram
authors.forename: Christina M
authors.initials: CM
authors.lastname: Lill
authors.forename: Joshua T
authors.initials: JT
authors.lastname: Cohen
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Trikalinos
mesh_terms: D006801:Humans; D000544:Alzheimer Disease / Q000235:genetics; D003362:Cost-Benefit Analysis; D057225:Data Mining / Q000379:methods; D016208:Databases, Factual; D036262:Empirical Research; D015201:Meta-Analysis as Topic; D010300:Parkinson Disease / Q000235:genetics; D010506:Periodicals as Topic; D012559:Schizophrenia / Q000235:genetics; D012984:Software; D013673:Technology Assessment, Biomedical; D000078202:Systematic Reviews as Topic