ArticleeLife2019
Linking glycemic dysregulation in diabetes to symptoms, comorbidities, and genetics through EHR data mining.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- Immunometabolic transcriptomic signatures of diabetes-related genes predict kidney transplant rejection.Renal failure · 2026Article
- Saliva as a potential and non-invasive approach to identify upregulated genes associated with comorbidities of T1DM: a brief report.European journal of medical research · 2025Article
- Subgrouping patients with ischemic heart disease by means of the Markov cluster algorithm.Communications medicine · 2025Article
- Cluster analysis and visualisation of electronic health records data to identify undiagnosed patients with rare genetic diseases.Scientific reports · 2024Article
- Precision subclassification of type 2 diabetes: a systematic review.Communications medicine · 2023Article
- Extracting Significant Comorbid Diseases from MeSH Index of PubMed.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database.Diabetes spectrum : a publication of the American Diabetes Association · 2022Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
- Current Pharmacological Treatment of Type 2 Diabetes Mellitus in Undocumented Migrants: Is It Appropriate for the Phenotype of the Disease?International journal of environmental research and public health · 2020Article
Corrections and comments
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Authors and funding
21 authors at 3 institutions in 1 country.
Funding
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 (Caucasian diabetes patients from Denmark) so the resulting stratification is not driven by ethnic differences, but rather by inherently dissimilar progression patterns and lifestyle related risk factors. Using unsupervised Markov clustering, we defined 71 clusters of at least 50 individuals within the diabetes spectrum. The clusters display both distinct and shared longitudinal glycemic dysregulation patterns, temporal co-occurrences of comorbidities, and associations to single nucleotide polymorphisms in or near genes relevant for diabetes comorbidities.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.