Artif Intell Med - Sepsis mortality prediction with the Quotient Basis Kernel.

Tópicos

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Resumo

JECTIVE: This paper presents an algorithm to assess the risk of death in patients with sepsis. Sepsis is a common clinical syndrome in the intensive care unit (ICU) that can lead to severe sepsis, a severe state of septic shock or multi-organ failure. The proposed algorithm may be implemented as part of a clinical decision support system that can be used in combination with the scores deployed in the ICU to improve the accuracy, sensitivity and specificity of mortality prediction for patients with sepsis.METHODOLOGY: In this paper, we used the Simplified Acute Physiology Score (SAPS) for ICU patients and the Sequential Organ Failure Assessment (SOFA) to build our kernels and algorithms. In the proposed method, we embed the available data in a suitable feature space and use algorithms based on linear algebra, geometry and statistics for inference. We present a simplified version of the Fisher kernel (practical Fisher kernel for multinomial distributions), as well as a novel kernel that we named the Quotient Basis Kernel (QBK). These kernels are used as the basis for mortality prediction using soft-margin support vector machines. The two new kernels presented are compared against other generative kernels based on the Jensen-Shannon metric (centred, exponential and inverse) and other widely used kernels (linear, polynomial and Gaussian). Clinical relevance is also evaluated by comparing these results with logistic regression and the standard clinical prediction method based on the initial SAPS score.RESULTS: As described in this paper, we tested the new methods via cross-validation with a cohort of 400 test patients. The results obtained using our methods compare favourably with those obtained using alternative kernels (80.18% accuracy for the QBK) and the standard clinical prediction method, which are based on the basal SAPS score or logistic regression (71.32% and 71.55%, respectively). The QBK presented a sensitivity and specificity of 79.34% and 83.24%, which outperformed the other kernels analysed, logistic regression and the standard clinical prediction method based on the basal SAPS score.CONCLUSION: Several scoring systems for patients with sepsis have been introduced and developed over the last 30 years. They allow for the assessment of the severity of disease and provide an estimate of in-hospital mortality. Physiology-based scoring systems are applied to critically ill patients and have a number of advantages over diagnosis-based systems. Severity score systems are often used to stratify critically ill patients for possible inclusion in clinical trials. In this paper, we present an effective algorithm that combines both scoring methodologies for the assessment of death in patients with sepsis that can be used to improve the sensitivity and specificity of the currently available methods.

Resumo Limpo

jectiv paper present algorithm assess risk death patient sepsi sepsi common clinic syndrom intens care unit icu can lead sever sepsi sever state septic shock multiorgan failur propos algorithm may implement part clinic decis support system can use combin score deploy icu improv accuraci sensit specif mortal predict patient sepsismethodolog paper use simplifi acut physiolog score sap icu patient sequenti organ failur assess sofa build kernel algorithm propos method emb avail data suitabl featur space use algorithm base linear algebra geometri statist infer present simplifi version fisher kernel practic fisher kernel multinomi distribut well novel kernel name quotient basi kernel qbk kernel use basi mortal predict use softmargin support vector machin two new kernel present compar generat kernel base jensenshannon metric centr exponenti invers wide use kernel linear polynomi gaussian clinic relev also evalu compar result logist regress standard clinic predict method base initi sap scoreresult describ paper test new method via crossvalid cohort test patient result obtain use method compar favour obtain use altern kernel accuraci qbk standard clinic predict method base basal sap score logist regress respect qbk present sensit specif outperform kernel analys logist regress standard clinic predict method base basal sap scoreconclus sever score system patient sepsi introduc develop last year allow assess sever diseas provid estim inhospit mortal physiologybas score system appli critic ill patient number advantag diagnosisbas system sever score system often use stratifi critic ill patient possibl inclus clinic trial paper present effect algorithm combin score methodolog assess death patient sepsi can use improv sensit specif current avail method

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