Prediction of MSOF Evolution by Means of Nine Vital Systems Trajectories

  • R. Pizzi
  • A. DeGaetano
  • P. Guadalupi
  • O. Chiara
  • C. Columbano
  • F. Sicurello
Conference paper
Part of the Lecture Notes in Medical Informatics book series (LNMED, volume 36)

Keywords

Injury Severity Score Emergency Medical Service Emergency Medical Service System Multiple System Organ Failure Multiple Organ Failure Syndrome 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Zusammenfassung

Eine Datenbasis wird vorgestellt, die von 20 Patienten mit postoperativen multiplen Organversagen (MSOF) abgeleitet wurde. Mittels dieses Materials wird versucht, eine formale Beschreibung des Verlaufs der Frtihsymptome abzuleiten. In einem ersten Schritt wird ein Bayes’scherAnsatz versucht. Die weiteren Arbeiten erfolgen mittels Markov-Ketten und Petri -Netze. Ziel der Autoren ist die formale Darstellung von dynamischen Zusammenhängen als Grundlage weiterer Arbeiten zur Entscheidungsunterstützung bei Patienten mit Verdacht auf MSOF.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. (1).
    CARRICO C.J. et al.: Multiple organ failure syndrome–incidence and problems. Arch. Surg. 121: 196–208, 1986.PubMedCrossRefGoogle Scholar
  2. (2).
    BAKER S.P. et al.: The injury severity score: A method for describing patients with multiple injuries and evaluating emergency care. J.Trauma 14: 187–196, 1974PubMedCrossRefGoogle Scholar
  3. (3).
    BEVILACQUA G. et al.: Il paziente di terapia intensiva: Database modulare per la gestione e l’elaborazione dei dati rilevati. Urg. Chir. Comm. 10,2: 67–73, 1987Google Scholar
  4. (4).
    KNAUS W.A. et al.: Apache II: A severity of disease classification system. Crit. Care Med. 13: 818–829, 1985PubMedCrossRefGoogle Scholar
  5. (5).
    GORIS R.J.A. et al.: Multiple organ failure–generalized autodestructive inflammation. Arch. Surg. 120: 1109–1115, 1985PubMedCrossRefGoogle Scholar
  6. (6).
    CHIARA O. et al.: A prospective evaluation of the critically ill surgical patient by means of physiologic trajectories. Acta of VIII Intern. Congr. of Emergency Surgery, 1985.Google Scholar
  7. (7).
    AJMONE-MARSAN M. et al.: Performance models of multiprocessor systems. Computer System Series, M.I.T. Press, 1986Google Scholar
  8. (8).
    GARFINKEL D.: Modelling technologies and techniques; the application of A.I. to modelling physiological systems. Mathematics and Computers in Biomedical ApplicationsGoogle Scholar
  9. (9).
    PETRI C.A.: Interpretation of net theory. GMD Bonn, Internal Report, ISF-75–07, 1976Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1988

Authors and Affiliations

  • R. Pizzi
    • 1
    • 2
  • A. DeGaetano
    • 1
    • 3
    • 4
  • P. Guadalupi
    • 1
    • 3
  • O. Chiara
    • 1
  • C. Columbano
    • 2
    • 3
  • F. Sicurello
    • 2
    • 3
  1. 1.Istituto di Chirurgia d’UrgenzaUniversita degli Studi di MilanoItaly
  2. 2.Dipartimento di Scienze dell’InformazioneUniversita degli Studi di MilanoItaly
  3. 3.Direzione ScientificaOspedale Maggiore Policlinico di MilanoItaly
  4. 4.Maryland Institute of Emergency Medical Services SystemBaltimoreUSA

Personalised recommendations