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

• 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: Article
• Language: English
• Published: 2024
• Subjects: Medicine, Health and Life Sciences; Machine learning; Prediction model
• Online Access: https://hdl.handle.net/10356/178674
• Institution: Nanyang Technological University

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...