Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining

Diabetes is a diverse and complex disease, with considerable variation in phenotypic manifestation and severity. This variation hampers the study of etiological differences and reduces the statistical power of analyses of associations to genetics, treatment outcomes, and complications. We address th...

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Bibliographic Details
Main Authors: Isa Kristina Kirk, Christian Simon, Karina Banasik, Peter Christoffer Holm, Amalie Dahl Haue, Peter Bjødstrup Jensen, Lars Juhl Jensen, Cristina Leal Rodríguez, Mette Krogh Pedersen, Robert Eriksson, Henrik Ullits Andersen, Thomas Almdal, Jette Bork-Jensen, Niels Grarup, Knut Borch-Johnsen, Oluf Pedersen, Flemming Pociot, Torben Hansen, Regine Bergholdt, Peter Rossing, Søren Brunak
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2019-12-01
Series:eLife
Subjects:
diabetes
EHR
text mining
diabetes subtypes
comorbidities
genotyping
Online Access:https://elifesciences.org/articles/44941

Internet

https://elifesciences.org/articles/44941

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