|Title||Renal Failure and Mortality: From Evidence to Artificial Intelligence, Change of Paradigm?|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Ibeas, J, Macias, E, Rubiella, C, Morell, A, Serrano, J, Rodriguez-Jornet, A, Vicario, J, Rexachs, D|
|Journal||Nephrology Dialysis Transplantation|
|Keywords||artificial intelligence, kidney failure, mortality|
The mortality of the patient with renal insufficiency is high and especially in dialysis. There are many risk factors involved, although mainly those related to cardiovascular risk, which in turn are closely linked to those related to uremia, mutually reinforcing. The approach to identifying these factors is difficult, and those recommended by Guides or predictive models have not been validated in the renal patient. Mortality risk models implicitly assume that each risk factor is linearly related to events, simplifying what are really complex relationships that would include a huge number of factors, with non-linear relationships. Approaches that incorporate multiple elements that identify real relationships are needed. Machine-learning can be an alternative. Based on computational methods that detect complex and non-linear interactions between variables identify latent variables, unlikely to observe directly.