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The research reported in this document deals with the problem of Sepsis data analysis in general and, more specifically, with the problem of survival prediction for patients affected with Severe Sepsis. The tools at the core of the
investigated data analysis procedures stem from the fields of multivariate and algebraic statistics, algebraic geometry, machine learning and computational intelligence.
Beyond data analysis itself, the current thesis makes contributions from a clinical point of view, as it presents a latent set of Sepsis descriptors to be used as prognostic factors for the prediction of mortality and achieves an improvement on predictive capability over indicators currently in use.. The focus of this project is studying the relation between different clinical traits and prognosis indicators widely used in clinical practice for the assessment of the risk of death in the Intensive Care Unit. It is clear that the most widely used indicators lack specificity in the assessment of risk of death for septic patients and, therefore, it is proposed to investigate potential improvements for this particular condition. The results will be verified through comparison with real data from a real ICU.
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