Associated factors of white matter hyperintensity volume: a machine-learning approach
Associated factors of white matter hyperintensity volume: a machine-learning approach
About this item
Full title
Author / Creator
Grosu, Sergio , Rospleszcz, Susanne , Hartmann, Felix , Habes, Mohamad , Bamberg, Fabian , Schlett, Christopher L. , Galie, Franziska , Lorbeer, Roberto , Auweter, Sigrid , Selder, Sonja , Buelow, Robin , Heier, Margit , Rathmann, Wolfgang , Mueller-Peltzer, Katharina , Ladwig, Karl-Heinz , Grabe, Hans J. , Peters, Annette , Ertl-Wagner, Birgit B. and Stoecklein, Sophia
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of...
Alternative Titles
Full title
Associated factors of white matter hyperintensity volume: a machine-learning approach
Authors, Artists and Contributors
Author / Creator
Rospleszcz, Susanne
Hartmann, Felix
Habes, Mohamad
Bamberg, Fabian
Schlett, Christopher L.
Galie, Franziska
Lorbeer, Roberto
Auweter, Sigrid
Selder, Sonja
Buelow, Robin
Heier, Margit
Rathmann, Wolfgang
Mueller-Peltzer, Katharina
Ladwig, Karl-Heinz
Grabe, Hans J.
Peters, Annette
Ertl-Wagner, Birgit B.
Stoecklein, Sophia
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_3e861650a14c4153b0cc0080e7208f3d
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_3e861650a14c4153b0cc0080e7208f3d
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-81883-4
