Diabetes mellitus (DM) is a metabolic disease based on insulin resistance or deficiency, characterized by chronically elevated blood glucose levels and associated with an increased risk of severe concomitant and secondary diseases. There are two main forms, DM type 1 and type 2, the latter of which can have highly heterogeneous manifestations, so that a more refined classification can contribute to more individualized treatment of high-risk patients with a potentially worse inpatient outcome.This study applies machine learning-based clustering to identify type 2 diabetes subphenotypes in a large German inpatient cohort and to assess the reproducibility of the clusters reported by Ahlqvist et al. (2018, The Lancet Diabetes & Endocrinology). For this purpose, data from Leipzig University Hospital was requested via the Data Integration Center.
Programme: Medical Data Science
LHA ID: 91TYCK33FX-2
Public web page: Not specified
Human Diseases: No Human Disease specified
Health Atlas - Local Data Hub/Leipzig PALs: No PALs for this Project
Project Coordinators: No Project coordinators for this Project
Project created: 8th Jul 2026
Overview
https://orcid.org/0009-0007-0838-6277