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Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase

Aushev, Alexander
Ripoll, Vicent Ribas
Vellido, Alfredo
Aletti, Federico
Herpain, Antoine
Post, Emiel Hendrik
Medina, Eduardo Romay
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Published in PLOS ONE. 2018, vol. 13, no. 11, p. e0199089
Abstract Circulatory shock is a life-threatening disease that accounts for around one-third of all admissions to intensive care units (ICU). It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. In this study, the ShockOmics European project original database is used to extract attributes capable of predicting mortality due to shock in the ICU. Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. Selected features were later used to build Bayesian Networks, revealing causal relationships between features and ICU outcome. The main result is a subset of predictive features that includes well-known indicators such as the SOFA and APACHE II scores, but also less commonly considered ones related to cardiovascular function assessed through echocardiograpy or shock treatment with pressors. Importantly, certain selected features are shown to be most predictive at certain time-steps. This means that, as shock progresses, different attributes could be prioritized. Clinical traits obtained at 24h. from ICU admission are shown to accurately predict cardiogenic and septic shock mortality, suggesting that relevant life-saving decisions could be made shortly after ICU admission.
PMID: 30457997
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Research group Groupe de recherche en hémodynamique (913)
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AUSHEV, Alexander et al. Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase. In: PLOS ONE, 2018, vol. 13, n° 11, p. e0199089. doi: 10.1371/journal.pone.0199089 https://archive-ouverte.unige.ch/unige:115362

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Deposited on : 2019-03-26

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