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

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 indic

SUBMITTER: Aushev A 

PROVIDER: S-EPMC6245679 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature