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

ABSTRACT: 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 these issues through deep, fine-grained phenotypic stratification of a diabetes cohort. Text mining the electronic health records of 14,017 patients, we matched two controlled vocabularies (ICD-10 and a custom vocabulary developed at the clinical center Steno Diabetes Center Copenhagen) to clinical narratives spanning a 19 year period. The two matched vocabularies comprise over 20,000 medical terms describing symptoms, other diagnoses, and lifestyle factors. The cohort is genetically homogeneous (

SUBMITTER: Kirk IK 

PROVIDER: S-EPMC6904221 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature