Mortality prediction enhancement in end-stage renal disease
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 f...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:273762 |
| Acesso em linha: | https://ddd.uab.cat/record/273762 https://dx.doi.org/urn:doi:10.1016/j.imu.2020.100351 |
| Access Level: | acceso abierto |
| Palavra-chave: | End-stage renal disease LSTM Machine learning Mortality prediction Random forest |
| Resumo: | 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. |
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