TY - JOUR
KW - Humans
KW - Prognosis
KW - Electronic Health Records
KW - *Renal Insufficiency, Chronic
KW - machine learning
KW - *Cardiovascular Diseases
KW - CKD subtype
KW - Cluster Analysis
KW - Survival analysis
KW - Unsupervised clustering
AU - A. Dashtban
AU - M. Mizani
AU - L. Pasea
AU - S. Denaxas
AU - R. Corbett
AU - J. Mamza
AU - H. Gao
AU - T. Morris
AU - H. Hemingway
AU - A. Banerjee
AD - Institute of Health Informatics, University College London, London, UK.
Institute of Health Informatics, University College London, London, UK; British Heart Foundation Data Science Centre, Health Data Research UK, London, UK.
Imperial College Healthcare NHS Trust, London, UK.
Medical and Scientific Affairs, BioPharmaceuticals Medical, AstraZeneca, London, UK.
Institute of Health Informatics, University College London, London, UK; Health Data Research UK, University College London, London, UK.
Institute of Health Informatics, University College London, London, UK; Barts Health NHS Trust, London, UK; University College London Hospitals NHS Trust, London, UK. Electronic address: ami.banerjee@ucl.ac.uk.
AN - 36857859
BT - EBioMedicine
C2 - PMC9989643
DO - 10.1016/j.ebiom.2023.104489
DP - NLM
ET - 2023/03/02
LA - eng
N1 - 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.
PY - 2023
SN - 2352-3964
EP - 104489
T2 - EBioMedicine
TI - Identifying subtypes of chronic kidney disease with machine learning: development, internal validation and prognostic validation using linked electronic health records in 350,067 individuals
VL - 89
ER -