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Kalman Filter zur On-Line-Diskriminanz-Analyse von Verlaufskurven

  • Conference paper
Multivariate Modelle

Part of the book series: Medizinische Informatik, Biometrie und Epidemiologie ((MEDINFO,volume 74))

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Zusammenfassung

Bisher vorgeschlagene Methoden für die sequentielle Zuordnung bzw. Prognosestellung (z.B. bei Patienten) unter Verwendung wiederholter Messungen werden skizziert. Zur Beurteilung dieser Methoden wird das Zuordnungsproblem in einem allgemeinen parametrischen Rahmen formuliert. Es zeigt sich, daß ein Bayes’scher Zugang der natürlichste ist und die Diskriminanzfunktion auf einem Filtersystem basiert. Für die Verlaufskurven wird eine Modellklasse zugrundegelegt, welches den von Laird & Ware (1982) beschriebenen Modellen für Repeated Measurements verwandt ist. Es wird ein sequentieller Diskriminanzanalyse - Algorithmus entwickelt, der auf einem nichtlinearen (adaptiven) Filter für das Trainings - Sample und einer Kombination von zwei Kaiman - Filtern für die Diskriminanzfunktion beruht. Schließlich analysieren wir ein biologisches Beispiel.

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Authors and Affiliations

  1. Institut für Biostatistik, Universität Innsbruck, Schöpfstrasse 41, A-6020, Innsbruck, Austria

    Willi-Julius Stronegger

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  1. Willi-Julius Stronegger
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Editors and Affiliations

  1. Institut für Statistik, Leopold-Franzens Universität Innsbruck, Innrain 52, A-6020, Innsbruck, Austria

    Gilg U. H. Seeber

  2. Institut für Sozial- und Präventivmedizin, Universität Bern, Finkenhubelweg 11, CH-3012, Bern, Switzerland

    Christoph E. Minder

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© 1991 Springer-Verlag Berlin Heidelberg

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Stronegger, WJ. (1991). Kalman Filter zur On-Line-Diskriminanz-Analyse von Verlaufskurven. In: Seeber, G.U.H., Minder, C.E. (eds) Multivariate Modelle. Medizinische Informatik, Biometrie und Epidemiologie, vol 74. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-95669-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-95669-0_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54511-8

  • Online ISBN: 978-3-642-95669-0

  • eBook Packages: Springer Book Archive

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