An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well s...

Full description

Bibliographic Details
Main Authors: Slieker, Roderick C., Münch, Magnus, Donnelly, Louise A., Bouland, Gerard A., Dragan, Iulian, Kuznetsov, Dmitry, Elders, Petra J. M., Rutter, Guy A., Ibberson, Mark, Pearson, Ewan R., Hart, Leen M. 't, van de Wiel, Mark A., Beulens, Joline W. J.
Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
Format: Journal Article
Language:English
Published: 2024
Subjects:
Medicine, Health and Life Sciences
Machine learning
Prediction model
Online Access:https://hdl.handle.net/10356/178674

Internet

https://hdl.handle.net/10356/178674

Similar Items