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Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers
A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical na...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7967733/ https://www.ncbi.nlm.nih.gov/pubmed/33671029 http://dx.doi.org/10.3390/ijerph18052364 |
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author | Gómez-Pulido, Juan A. Gómez-Pulido, José M. Rodríguez-Puyol, Diego Polo-Luque, María-Luz Vargas-Lombardo, Miguel |
author_facet | Gómez-Pulido, Juan A. Gómez-Pulido, José M. Rodríguez-Puyol, Diego Polo-Luque, María-Luz Vargas-Lombardo, Miguel |
author_sort | Gómez-Pulido, Juan A. |
collection | PubMed |
description | A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%. |
format | Online Article Text |
id | pubmed-7967733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79677332021-03-18 Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers Gómez-Pulido, Juan A. Gómez-Pulido, José M. Rodríguez-Puyol, Diego Polo-Luque, María-Luz Vargas-Lombardo, Miguel Int J Environ Res Public Health Article A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%. MDPI 2021-02-28 /pmc/articles/PMC7967733/ /pubmed/33671029 http://dx.doi.org/10.3390/ijerph18052364 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gómez-Pulido, Juan A. Gómez-Pulido, José M. Rodríguez-Puyol, Diego Polo-Luque, María-Luz Vargas-Lombardo, Miguel Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers |
title | Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers |
title_full | Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers |
title_fullStr | Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers |
title_full_unstemmed | Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers |
title_short | Predicting the Appearance of Hypotension during Hemodialysis Sessions Using Machine Learning Classifiers |
title_sort | predicting the appearance of hypotension during hemodialysis sessions using machine learning classifiers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7967733/ https://www.ncbi.nlm.nih.gov/pubmed/33671029 http://dx.doi.org/10.3390/ijerph18052364 |
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