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  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., 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.

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Wallace.pdf (Verlagsversion), 364KB
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© 2012 Nature Publishing Group
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 Urheber:
Wallace, B. C., Autor
Small, K., Autor
Brodley, C. E., Autor
Lau, J., Autor
Schmid, C. H., Autor
Bertram, L.1, Autor           
Lill, C. M.1, 2, Autor           
Cohen, J. T., Autor
Trikalinos, T. A., Autor
Affiliations:
1Neuropsychiatric Genetics (Lars Bertram), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, Berlin, Germany, ou_1479655              
2Department of Neurology, University Medical Center of the Johannes Gutenberg-University, Mainz, Germany, ou_persistent22              

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Schlagwörter: Alzheimer Disease/genetics Cost-Benefit Analysis Data Mining/*methods Databases, Factual Empirical Research Humans Meta-Analysis as Topic Parkinson Disease/genetics Periodicals as Topic *Review Literature as Topic Schizophrenia/genetics Software Technology Assessment, Biomedical
 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2012-04-052012-07
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1038/gim.2012.7
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Titel: GENETICS IN MEDICINE
  Andere : Genet. Med.
Genre der Quelle: Zeitschrift
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Affiliations:
Ort, Verlag, Ausgabe: Baltimore, MD : Williams & Wilkins
Seiten: - Band / Heft: 14 (7) Artikelnummer: - Start- / Endseite: 663 - 669 Identifikator: ISSN: 1098-3600
CoNE: https://pure.mpg.de/cone/journals/resource/954925610933