Survival in the Intensive Care Unit: a prognosis model based on Bayesian Classifiers

R. Delgado, José David NUÑEZ GONZALEZ*, Juan Carlos Yébenes, Ángel Lavado

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)


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.

Translated title of the contributionSupervivencia en la Unidad de Cuidados Intensivos: un modelo de pronóstico basado en Clasificadores Bayesianos
Original languageEnglish
Article number102054
Pages (from-to)102054
Number of pages26
JournalArtificial Intelligence in Medicine
Publication statusPublished - 1 May 2021


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