@article{10770,
  keywords = {Humans, Prognosis, Electronic Health Records, *Renal Insufficiency, Chronic, machine learning, *Cardiovascular Diseases, CKD subtype, Cluster Analysis, Survival analysis, Unsupervised clustering},
  author = {A. Dashtban and M. Mizani and L. Pasea and S. Denaxas and R. Corbett and J. Mamza and H. Gao and T. Morris and H. Hemingway and A. Banerjee},
  title = {Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals},
  year = {2023},
  journal = {EBioMedicine},
  volume = {89},
  edition = {2023/03/02},
  pages = {104489},
  isbn = {2352-3964},
  doi = {10.1016/j.ebiom.2023.104489},
  note = {2352-3964
Dashtban, Ashkan
Mizani, Mehrdad A
Pasea, Laura
Denaxas, Spiros
Corbett, Richard
Mamza, Jil B
Gao, He
Morris, Tamsin
Hemingway, Harry
Banerjee, Amitava
Journal Article
Netherlands
EBioMedicine. 2023 Mar;89:104489. doi: 10.1016/j.ebiom.2023.104489. Epub 2023 Feb 27.},
  language = {eng},
}