Mortality prediction enhancement in end-stage renal disease : A machine learning approach
Macias Toro, Edwar Hernando (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Morell Pérez, Antoni (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Serrano García, Javier 1964- (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
López Vicario, José (Universitat Autònoma de Barcelona. Departament de Telecomunicació i Enginyeria de Sistemes)
Ibeas, Jose (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT))

Date:	2020
Abstract:	In this work, we propose to combine massive variables collected during the evolution of patients in end-stage renal disease (ESRD), along with machine learning techniques to improve mortality prediction in ESRD. This work was carried out with a retrospective cohort of 261 patients, their evolution from diagnoses, laboratory tests, and variables recorded during haemodialysis sessions was combined. Random forest (RF) was used to explore the inference of the variables and define a base performance for long short-term memory (LSTM) recurrent neural networks. Then, LSTMs were trained with several groups of variables chosen by expert staff, the ones found by RF and all the available ones. The best performance was obtained using all the variables, but the ones found by RF had better predictive capacity than those chosen with expert knowledge. Integrating the three sources of information supposes an improvement in more than 4% in the area under the receiver operating characteristic curve. The approach is sufficientlyrobust to predict mortality at different time ranges. The massive integration of variables from patients in ESRD, together with the use of LSMTs, supposes an exceptional improvement in the predictive models of mortality. In conclusion, the machine learning approach can lead to a change in the paradigm in the analysis of predictive factors in mortality in ESRD.
Grants:	Agencia Estatal de Investigación TEC2017-84321-C4-4-R Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1670
Rights:	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.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	End-stage renal disease ; LSTM ; Machine learning ; Mortality prediction ; Random forest
Published in:	Informatics in Medicine Unlocked, Vol. 19 (May 2020) , art. 100351, ISSN 2352-9148

DOI: 10.1016/j.imu.2020.100351

10 p, 1.6 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Parc Taulí Research and Innovation Institute (I3PT
Articles > Research articles
Articles > Published articles