Informatik aktuell, pages 180-185
Clustering Socio-Demographic and Medical Attribute Data in Cohort Studies
Publication type: Book Chapter
Publication date: 2014-03-03
Abstract
Longitudinal epidemiological studies like the Study of Health in Pomerania (SHIP) analyze a group of thousands of subjects (a cohort) by imposing a multitude of socio-demographic and biological factors. Epidemiological findings rest upon hypotheses which yield a selection of disease-specific cohort study parameters. They are then analyzed for significant interactions to identify risk factors. We propose an alternative approach by incorporating clustering algorithms with a Visual Analytics system to form subject groups which are the basis for an exploratory analysis of the underlying parameter interactions. We investigated three clustering techniques (k-Prototypes, DBSCAN and hierarchical clustering) for their suitability in these data sets. With our system, groups can be automatically determined to provide insights into this complex data.
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Klemm P. et al. Clustering Socio-Demographic and Medical Attribute Data in Cohort Studies // Informatik aktuell. 2014. pp. 180-185.
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Klemm P., Frauenstein L., Perlich D., Hegenscheid K., Völzke H., Preim B. Clustering Socio-Demographic and Medical Attribute Data in Cohort Studies // Informatik aktuell. 2014. pp. 180-185.
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TY - GENERIC
DO - 10.1007/978-3-642-54111-7_36
UR - https://doi.org/10.1007/978-3-642-54111-7_36
TI - Clustering Socio-Demographic and Medical Attribute Data in Cohort Studies
T2 - Informatik aktuell
AU - Klemm, Paul
AU - Frauenstein, Lisa
AU - Perlich, David
AU - Hegenscheid, Katrin
AU - Völzke, Henry
AU - Preim, Bernhard
PY - 2014
DA - 2014/03/03
PB - Springer Nature
SP - 180-185
SN - 1431-472X
ER -
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@incollection{2014_Klemm,
author = {Paul Klemm and Lisa Frauenstein and David Perlich and Katrin Hegenscheid and Henry Völzke and Bernhard Preim},
title = {Clustering Socio-Demographic and Medical Attribute Data in Cohort Studies},
publisher = {Springer Nature},
year = {2014},
pages = {180--185},
month = {mar}
}