Mortality prediction enhancement in end-stage renal disease: A machine learning approach

Edwar Macias, Antoni Morell, Javier Serrano, Jose Lopez Vicario, Jose Ibeas

Research output: Contribution to journalArticleResearchpeer-review


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 sufficiently robust 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.

Original languageAmerican English
Article number100351
JournalInformatics in Medicine Unlocked
Publication statusPublished - 2020


  • End-stage renal disease
  • LSTM
  • Machine learning
  • Mortality prediction
  • Random forest

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