TY - JOUR
T1 - Survival in the Intensive Care Unit: a prognosis model based on Bayesian Classifiers
AU - Delgado, R.
AU - NUÑEZ GONZALEZ, José David
AU - Yébenes, Juan Carlos
AU - Lavado, Ángel
N1 - Copyright © 2021 Elsevier B.V. All rights reserved.
PY - 2021/5/1
Y1 - 2021/5/1
N2 - 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.
AB - 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.
KW - Unidad de Cuidados Intensivos; Riesgo de mortalidad; Clasificador Bayesiano; Ensemble; Area Under the Curve; F-score; APACHE II
UR - http://www.scopus.com/inward/record.url?scp=85103686674&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/2298e9d3-bbde-343c-9a74-f8e5213be36c/
U2 - 10.1016/j.artmed.2021.102054
DO - 10.1016/j.artmed.2021.102054
M3 - Article
C2 - 34001314
SN - 0933-3657
VL - 115
SP - 102054
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 102054
ER -