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

• المؤلفون الرئيسيون: 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.
• مؤلفون آخرون: Lee Kong Chian School of Medicine (LKCMedicine)
• التنسيق: مقال
• اللغة: English
• منشور في: 2024
• الموضوعات: Medicine, Health and Life Sciences; Machine learning; Prediction model
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/178674
• المؤسسة: 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...