dc.contributor.author | Delgado de la Torre, Rosario | |
dc.contributor.author | Núñez González, José David | |
dc.contributor.author | Yébenes, Juan Carlos | |
dc.contributor.author | Lavado, Ángel | |
dc.date.accessioned | 2024-05-22T14:43:42Z | |
dc.date.available | 2024-05-22T14:43:42Z | |
dc.date.issued | 2021-05 | |
dc.identifier.citation | Artificial Intelligence in Medicine 115 : (2021) // Article ID 102054 | es_ES |
dc.identifier.issn | 1873-2860 | |
dc.identifier.issn | 0933-3657 | |
dc.identifier.uri | http://hdl.handle.net/10810/68108 | |
dc.description.abstract | We develop a predictive prognosis model to support medical experts in their clinical decision-making process in Intensive Care Units (ICUs) (a) to enhance early mortality prediction, (b) to make more efficient medical decisions about patients at higher risk, and (c) to evaluate the effectiveness of new treatments or detect changes in clinical practice. It is a machine learning hierarchical model based on Bayesian classifiers built from some recorded features of a real-world ICU cohort, to bring about the assessment of the risk of mortality, also predicting destination at ICU discharge if the patient survives, or the cause of death otherwise, constructed as an ensemble of five base Bayesian classifiers by using the average ensemble criterion with weights, and we name it the Ensemble Weighted Average (EWA).
We compare EWA against other state-of-the-art machine learning predictive models. Our results show that EWA outperforms its competitors, presenting in addition the advantage over the ensemble using the majority vote criterion of allowing to associate a confidence level to the provided predictions. We also prove the convenience of locally recalibrate from data the standard model used to predict the mortality risk based on the APACHE II score, although as a predictive model it is weaker than the other. | es_ES |
dc.description.sponsorship | R. Delgado and J.D. Núñez-González are supported by Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project ref. PGC2018-097848-B-I0.
R. Delgado, J.D. Núñez-González, Juan Carlos Yébenes and Angel Lavado are partially supported by TV3 Fundació Marató (Sepsis Training, Audit and Feedback (STAF) Project; Codi Projecte 201836). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICIU/PGC2018-097848-B-I0 | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Intensive Care Unit | es_ES |
dc.subject | mortality risk | es_ES |
dc.subject | bayesian classifier ensemble | es_ES |
dc.subject | area under the curve | es_ES |
dc.subject | F-score | es_ES |
dc.subject | APACHE II | es_ES |
dc.title | Survival in the Intensive Care Unit: A prognosis model based on Bayesian classifiers | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0933365721000476 | es_ES |
dc.identifier.doi | 10.1016/j.artmed.2021.102054 | |
dc.departamentoes | Matemática aplicada | es_ES |
dc.departamentoeu | Matematika aplikatua | es_ES |