title: Severe sepsis mortality prediction with relevance vector machines.
abstract: Sepsis is a transversal pathology and one of the main causes of death at the Intensive Care Unit (ICU). It has in fact become the tenth most common cause of death in western societies. Its mortality rates can reach up to 45.7% for septic shock, its most acute manifestation. For these reasons, the prediction of the mortality caused by sepsis is an open and relevant medical research challenge. This problem requires prediction methods that are robust and accurate, but also readily interpretable. This is paramount if they are to be used in the demanding context of real-time decision making at the ICU. In this brief paper, such a method is presented. It is based on a variant of the well-known support vector machine (SVM) model and provides an automated ranking of relevance of the mortality predictors. The reported results show that it outperforms in terms of accuracy alternative techniques currently in use, while simultaneously assessing the relative impact of individual pathology indicators.
authors.affiliation: SOCO Research Group, Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain.
authors.forename: Vicent J
authors.initials: VJ
authors.lastname: Ribas
authors.forename: Jesús Caballero
authors.initials: JC
authors.lastname: López
authors.forename: Adolf
authors.initials: A
authors.lastname: Ruiz-Sanmartin
authors.forename: Juan Carlos
authors.initials: JC
authors.lastname: Ruiz-Rodríguez
authors.forename: Jordi
authors.initials: J
authors.lastname: Rello
authors.forename: Anna
authors.initials: A
authors.lastname: Wojdel
authors.forename: Alfredo
authors.initials: A
authors.lastname: Vellido
mesh_terms: D006801:Humans; D015994:Incidence; D011379:Prognosis; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D018805:Sepsis / Q000401:mortality*; D060388:Support Vector Machine; D016019:Survival Analysis; D015996:Survival Rate