Abstract
This paper proposes the application of new knowledge based methods to a septic shock patient database. It uses wrapper methods (bottom-up tree search or ant feature selection) to reduce the number of features. Fuzzy and neural modeling are used for classification. The goal is to estimate, as accurately as possible, the outcome (survived or deceased) of these septic shock patients. Results show that the approaches presented outperform any previous solutions, specifically in terms of sensitivity.
This work is supported by the Portuguese Government under the programs: project PTDC/SEM-ENR/100063/2008, Fundação para a Ciência e Tecnologia (FCT), and by the MIT-Portugal Program and FCT grants SFRH/43043/2008 and SFRH/43081/2008.
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Fialho, A.S. et al. (2010). Predicting Outcomes of Septic Shock Patients Using Feature Selection Based on Soft Computing Techniques. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_7
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DOI: https://doi.org/10.1007/978-3-642-14058-7_7
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