Abstract
In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies.
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References
Berthold, M. (1999), “Fuzzy logic,” Chapter 8 in Berthold, M. and Hand, D.J. (Eds.), Intelligent Data Analysis: an Introduction, Springer-Verlag, pp. 269–298.
Berthold, M. and Diamond, J. (1995), “Boosting the performance of RBF networks with dynamic decay adjustment,” Advances in Neural Information Processing Systems, vol. 7, pp. 521–528.
Berthold, M. and Diamond, J. (1998), “Constructive training of probabilistic neural networks,” Neurocomputing, vol. 19, pp. 167–183.
Brause, R. and Hanisch, E. (2000), Medical Data Analysis ISMDA 2000, Springer Lecture Notes in Comp.Sc., LNCS 1933, Springer Verlag, Heidelberg.
Brause, R. (1999), “Revolutionieren neuronale Netze unsere Vorhersagefähigkeiten?” Zentralblatt für Chirurgie, vol. 124, pp. 692–698.
Brause, R. and Friedrich, F. (2000), “A neuro-fuzzy approach as medical diagnostic interface,” Proc. ESANN 2000, De Facto Publ., Brussels, pp. 201–206.
Bruske, J. (1998), “Dynamische Zellstrukturen. Theorie und Anwendung eines KNN-Modells,” Dissertation, Technische Fakultät der Christian-Albrechts-Universität, Kiel, Germany.
Bruske, J. and Sommer, G. (1995), “Dynamic cell structure learns perfectly topology preserving map,” Neural Computation, vol. 7, pp. 845–865.
Bellman, R. (1961), Adaptive Control Processes: a Guided Tour, Princeton, NJ: Princeton University Press.
Dybowski, R. (1997), “Assigning confidence intervals to neural network predictions,” Technical Report, Division of Infection, UMDS (St Thomas’ Hospital), London, 2 March 1997, available at http://www.umds.ac.uk/microbio/richard/nnci.pdf.
Fein, A.M. et al. (Eds.) (1997), Sepsis and Multiorgan Failure, Williams and Wilkins, Baltimore.
Fritzke, B. (1994), “Fast learning with incremental RBF networks,” Neural Processing Letters, vol. 1, no. 1, pp. 2–5.
Fritzke, B. (1995), “A growing neural gas network learns topologies,” in: Tesauro, G., Touretzky, D.S., and Leen, T.K. (Eds.), Proc. Advances in Neural Information Processing Systems (NIPS 7), MIT Press, Cambridge, MA, pp. 625–632.
Fritzke, B. (1997), “Incremental neuro-fuzzy systems,” in: Bosacchi, B., Bezdek, J.C., and Fogel, D.B. (Eds.), Proc. SPIE, vol. 3165, Applications of Soft Computing, pp. 86–97.
Geisser, S. (1975), “The predictive sample reuse method with applications,” Journal of The American Statistical Association, vol. 70, pp. 320–328.
Hamker, F., Paetz, J., Thöne, S., Brause, R., and Hanisch, E. (2000), `Erkennung kritischer Zustände von Patienten mit der Diagnose `Septischer Schock’ mit einem RBF-Netz,“ Tech. Report, Interner Bericht 04/00, Fachbereich Informatik, J.W. Goethe-Uni-versität Frankfurt a. M., http://www.cs.uni-frankfurt.de/fbreports/ fbreport04–00.pdf.
Hartung, J. (1993), Statistik: Lehr-und Handbuch der Angewandten Statistik, Oldenbourg-Verlag, München.
Haykin, S. (1999), Neural Networks, a Comprehensive Foundation, Prentice Hall, 2nd edition, Upper Saddle River, NJ 07458.
Heinke, D. and Hamker, F. (1998), “Comparing neural networks, a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP,” IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1279–1291.
Huber, K.-P. and Berthold, M.R. (1995), “Building precise classifiers with automatic rule extraction,” IEEE International Conference on Neural Networks, vol. 3, pp. 1263–1268.
Inza I., Merino M., Larranaga P., Quiroga J., Sierra B., and Girala M. (2000), “Feature subset selection using probabilistic tree structures. a case study in the survival of cirrhotic patients treated with TIPS,” in [4], pp. 97–110.
Kindermann, L., Lewandowski, A., Tagscherer, M., and Protzel, P. (1999), “Computing confidence measures and marking unreliable predictions by estimating input data densities with MLPs,” Proceedings of the Sixth International Conference on Neural Information Processing (ICONIP ‘89), Perth, Australia, pp. 91–94.
Lavrac, N. (1999), “Machine learning for data mining in medicine,” in: Horn, W. et al (Eds.), Proc. AIMDM’99. LNAI 1620, Springer-Verlag, Berlin Heidelberg, pp. 47–62.
Martinetz, T.M. and Schulten, K.J. (1994), “Topology representing networks,” Neural Networks, vol. 7, pp. 507–522.
Members of the American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference Committee (1992), “Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis,” Crit. Care Med., vol. 20, pp. 864–874.
Mosteller, F. and Tukey, J.W. (1968), “Data analysis, including statistics,” in: Lindzey, G. and Aronson, E. (Eds.), Handbook of Social Psychology 2, Addison-Wesley.
Nauck, D. (1999), “Obtaining interpretable fuzzy classification rules from medical data,” Artificial Intelligence in Medicine, vol. 16, no. 2, pp. 149–169.
Neugebauer, E. and Lefering, R. (1996), “Scoresysteme und Datenbanken in der Intensivmedizin — Notwendigkeit und Grenzen,” Intensivmedizin, vol. 33, pp. 445–447.
Paetz, J., Hamker, F., and Thöne, S. (2000), “About the analysis of septic shock patient data,” in [4], pp. 130–137. Also available at http://www.cs.uni-frankfurt.de/~ paetz/PaetzISMDA2000.pdf.
Pietruschka, U. and Brause, R. (1999), “Using growing RBF nets in rubber industry process control,” Neural Computing and Applications, Springer Verlag, vol. 8, no. 2, pp. 95–105.
Schumacher, M., Rößner, R., and Vach, W. (1996), “Neural networks and logistic regression, part I,” Computational Statistics and Data Analysis, vol. 21, pp. 661–682.
Seely, A. and Christou, N. (2000), “Multiple organ dysfunction syndrome, exploring the paradigm of complex nonlinear systems,” Crit. Care Med., vol. 28, no. 7, pp. 2193–2200.
Tagscherer, M., Kindermann, L., Lewandowski, A., and Protzel, P. (1999), “Overcome neural limitations for real world applications by providing confidence values for network predictions,” Proceedings of the Sixth International Conference on Neural Information Processing (ICONIP ‘89), Perth, Australia, pp. 520–525.
Toweill, D., Sonnenthal, K., Kimberly, B., Lai, S., and Goldstein, B. (2000), “Linear and nonlinear analysis of hemodynamic signals during sepsis and septic shock,” Crit. Care Med., vol. 28, no. 6, pp. 2051–2057.
Vach, W., Roner, R., and Schumacher, M. (1996), “Neural networks and logistic regression: part II,” Computational Statistics and Data Analysis, vol. 21, 683–701.
Wade, S., Büssow, M, and Hanisch, E. (1998), “Epidemiology of systemic inflammatory response syndrome, sepsis and septic shock in surgical intensive care patients,” Chirurg, vol. 69, pp. 648–655.
Wahba, G. and Wold, S. (1975), “A completely automatic French curve: fitting spline functions by cross-validation,” Communications in Statistics, vol. 4, pp. 1–17.
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Brause, R., Hamker, F., Paetz, J. (2002). Septic Shock Diagnosis by Neural Networks and Rule Based Systems. In: Schmitt, M., Teodorescu, HN., Jain, A., Jain, A., Jain, S., Jain, L.C. (eds) Computational Intelligence Processing in Medical Diagnosis. Studies in Fuzziness and Soft Computing, vol 96. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1788-1_12
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DOI: https://doi.org/10.1007/978-3-7908-1788-1_12
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