Abstract
In abdominal intensive care medicine letality of septic shock patients is very high. In this contribution we present results of a data driven rule generation with categorical septic shock patient data, collected from 1996 to 1999. Our descriptive approach includes preprocessing of data for rule generation and application of an efficient algorithm for frequent patterns generation. Performance of generated rules is rated by frequency and confidence measures. The best rules are presented. They provide new quantitative insight for physicians with regard to septic shock patient outcome.
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Wade, S., Büssow, M., Hanisch, E.: Epidemiologie von SIRS, Sepsis und septischem Schock bei chirurgischen Intensivpatienten. Der Chirurg 69 (1998) 648–655
Schoenberg, M. H., Weiss, M., Radermacher, P.: Outcome of Patients with Sepsis and Septic Shock after ICU Treatment. Arch Surch 383 (1998) 44–48
Fein, A. M. et al. (eds.): Sepsis and Multiorgan Failure, Williams & Wilkins Baltimore (1997)
Hamker, F., Paetz, J., Thöne, S., Brause, R., Hanisch, E.: Erkennung kritischer Zustände von Patienten mit der Diagnose “Septischer Schock” mit einem RBFNetz. Interner Bericht 04/00, FB Informatik, J. W. Goethe-Univ. Frankfurt am Main, Germany (2000)
Paetz, J., Hamker, F., Thöne, S.: About the Analysis of Septic Shock Patient Data. 1st Int. Symp. on Medical Data Analysis (ISMDA). Frankfurt am Main, Germany. LNCS Vol. 1933. Springer-Verlag (2000) 130–137
Han, J., Pei, J., Yin, Y.: Mining Frequent PatternsWithout Candidate Generation. ACM SIGMOD Int. Conf. on Management of Data. Dallas, USA (2000) 1–12
Agrawal, R., Skrikant, R.: Fast Algorithms for Mining Association Rules. 20th Int. Conf. on Very Large Databases (VLDB). Santiago de Chile, Chile (1994) 487–499
Brause, R., Langsdorf, T., Hepp, M.: Neural Data Mining for Credit Card Fraud Detection. 11th IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI). Chicago, USA (1999) 103–106
Sedgewick, R.: Algorithms in C. Addison Wesley (1992)
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Paetz, J., Brause, R. (2001). A Frequent Patterns Tree Approach for Rule Generation with Categorical Septic Shock Patient Data. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_31
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DOI: https://doi.org/10.1007/3-540-45497-7_31
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