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Página principal > Artículos > Artículos publicados > Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records |
Fecha: | 2022 |
Resumen: | Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease. Once we obtained the latent space, we used classical clustering algorithms to create longitudinal clusters representing different evolutions of the diabetic disease. Our unsupervised DL clustering algorithm suggested seven different longitudinal clusters. Different mean ages were observed among the clusters (ranging from 65. 3±11. 6 to 72. 8±9. 4). Subjects in clusters B (Hypercholesteraemic) and E (Hypertensive) had shorter diabetes duration (9. 2±3. 9 and 9. 5±3. 9 years respectively). Subjects in Cluster G (Metabolic) had the poorest glycaemic control (mean glycated hemoglobin 7. 99±1. 42%), while cluster E had the best one (mean glycated hemoglobin 7. 04±1. 11%). Obesity was observed mainly in clusters A (Neuropathic), C (Multiple Complications), F (Retinopathy) and G. A dashboard is available at dm2. b2slab. upc. edu to visualize the different trajectories corresponding to the 7 clusters. |
Ayudas: | Ministerio de Economía y Competitividad TEC2014-60337-R Ministerio de Economía y Competitividad DPI2017-89827-R European Commission. Horizon 2020 780262 |
Nota: | Altres ajuts: Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN); Instituto de Investigación Carlos III (ISCIII); CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM). |
Derechos: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. |
Lengua: | Anglès |
Documento: | Article ; recerca ; Versió publicada |
Materia: | Type 2 diabetes ; Deep learning ; Longitudinal cluster ; AutoEncoder ; Diabetic complications ; Electronic health records |
Publicado en: | Journal of Biomedical Informatics, Vol. 135 (november 2022) , p. 104218, ISSN 1532-0464 |
9 p, 1.3 MB |