Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project

Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data...

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Veröffentlicht in:	PloS one Jg. 11; H. 8; S. e0160648
Hauptverfasser:	Roberto, Giuseppe, Leal, Ingrid, Sattar, Naveed, Loomis, A. Katrina, Avillach, Paul, Egger, Peter, van Wijngaarden, Rients, Ansell, David, Reisberg, Sulev, Tammesoo, Mari-Liis, Alavere, Helene, Pasqua, Alessandro, Pedersen, Lars, Cunningham, James, Tramontan, Lara, Mayer, Miguel A., Herings, Ron, Coloma, Preciosa, Lapi, Francesco, Sturkenboom, Miriam, van der Lei, Johan, Schuemie, Martijn J., Rijnbeek, Peter, Gini, Rosa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 31.08.2016 Public Library of Science (PLoS)
Schlagworte:	Age Algorithms Biology and Life Sciences Building components Causes of Chronic illnesses Collaboration Data Mining - methods Data sources Databases, Factual Derivation Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - epidemiology Diabetis Diagnostic systems Documentation Drugs Electronic health records Epidemiology Europe - epidemiology Female Genomes Health care Health informatics Heart Heterogeneity Hospitals Humans Information systems Male Medical diagnosis Medical informatics Medical records Medical research Medical screening Medicine and Health Sciences Mortality Physical Sciences Physicians Primary care Protocols clínics Research and Analysis Methods Source studies Standard data Type 2 diabetes United States New York Netherlands United Kingdom > UK United States > US Denmark Italy Aarhus Denmark Estonia Spain
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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