@article{10864,
  keywords = {Humans, Prognosis, Electronic Health Records, *atrial fibrillation, *Heart Failure/diagnosis/epidemiology, machine learning},
  author = {A. Banerjee and A. Dashtban and S. Chen and L. Pasea and J. Thygesen and G. Fatemifar and B. Tyl and T. Dyszynski and F. Asselbergs and L. Lund and T. Lumbers and S. Denaxas and H. Hemingway},
  title = {Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study},
  year = {2023},
  journal = {Lancet Digit Health},
  volume = {5},
  edition = {2023/05/27},
  number = {6},
  pages = {e370-e379},
  isbn = {2589-7500},
  doi = {10.1016/s2589-7500(23)00065-1},
  note = {2589-7500
Banerjee, Amitava
Dashtban, Ashkan
Chen, Suliang
Pasea, Laura
Thygesen, Johan H
Fatemifar, Ghazaleh
Tyl, Benoit
Dyszynski, Tomasz
Asselbergs, Folkert W
Lund, Lars H
Lumbers, Tom
Denaxas, Spiros
Hemingway, Harry
DH_/Department of Health/United Kingdom
MRC_/Medical Research Council/United Kingdom
CSO_/Chief Scientist Office/United Kingdom
BHF_/British Heart Foundation/United Kingdom
WT_/Wellcome Trust/United Kingdom
Journal Article
Research Support, Non-U.S. Gov't
England
Lancet Digit Health. 2023 Jun;5(6):e370-e379. doi: 10.1016/S2589-7500(23)00065-1.},
  language = {eng},
}