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Some Statistical Methods in Intensive Care Online Monitoring — A Review

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Medical Data Analysis (ISMDA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1933))

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Abstract

Intelligent alarm systems are needed for adequate bedside decision support in critical care. Clinical information systems acquire physiological variables online in short time intervals. To identify complications as well as therapeutic effects procedures for rapid classification of the current state of the patient have to be developed. Detection of characteristic patterns in the data can be accomplished by statistical time series analysis. In view of the high dimension of the data statistical methods for dimension reduction should be used in advance. We discuss the potential of statistical techniques for online monitoring.

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References

  1. Miller, G.: The Magical Number Seven, Plus or Minus Two: Some Limits to Our Capacity for Processing Information. Psychol. Rev. 63 (1956) 81–97

    Article  Google Scholar 

  2. Jennings, D., Amabile, T., Ross, L.: Informal Covariation Asessments: Data-Based Versus Theory-Based Judgements. In: Kahnemann, D., Slovic, P., Tversky, A. (eds.): Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge (1982) 211–230

    Google Scholar 

  3. Imhoff, M.: One Year Experience with a UNIX-based Clinical Information System (CIS) on a SICU. In: Lenz, K., Metnitz, P.G.H. (eds.): Patient Data Management in Intensive Care, Springer-Verlag, Wien (1993) 107–114

    Google Scholar 

  4. O’Carrol, T.M.: Survey of Alarms in an Intensive Therapy Unit. Anaesthesia 41 (1986) 742–744

    Article  Google Scholar 

  5. Miksch, S., Horn, W., Popow, C., Paky, F.: Utilizing Temporal Abstraction for Data Validation and Therapy Planning for Artificially Ventilated Newborne Infants. Art. Int. Med. 8 (1996) 543–576

    Article  Google Scholar 

  6. Haimowitz, I.J., Kohane, I.S.: Managing Temporal Worlds for Medical and Trend Diagnosis. Art. Int. Med. 8 (1996) 299–321

    Article  Google Scholar 

  7. Daumer, M.: Adaptive Drifterkennung und Intelligente Alarmsysteme. In: Biomedizinische Technik. Ergänzungsband. Schiele und Schoen (2000) to appear

    Google Scholar 

  8. Hill, D.W., Endresen, J.: Trend Recording and Forecasting in Intensive Care Therapy. Br. J. Clin. Equipment 1 (1978) 5–14

    Google Scholar 

  9. Gordon, K., Smith, A.S.M.: Modeling and Monitoring Biomedical Time Series. J. Am. Stat. Assoc. 85 (1990) 328–337

    Article  Google Scholar 

  10. Hepworth, J.T., Hendrickson, S.G., Lopez, J.: Time Series Analysis of Physiological Response During ICU Visitation. West J. Nurs. Res. 16 (1994) 704–717

    Article  Google Scholar 

  11. Imhoff, M., Bauer, M., Gather, U., Löhlein, D.: Time Series Analysis in Intensive Care Medicine. Applied Cardiopulmonary Pathophysiology 6 (1997) 263–281

    Google Scholar 

  12. Huber, P.J.: Massive Datasets Workshop: Four Years After. J. Comp. Graph. Stat. 8 (1999) 635–652

    Article  Google Scholar 

  13. Smith, A.F.M., West, M.: Monitoring Renal Transplants: an Application of the Multiprocess Kalman Filter. Biometrics 39 (1983) 867–878

    Article  MATH  Google Scholar 

  14. West, M., Harrison, J.: Bayesian Forecasting and Dynamic Models. Springer-Verlag, New York (1989)

    MATH  Google Scholar 

  15. Daumer, M., Falk, M.: On-line Change-Point Detection for State Space Models Using Multi-process Kalman Filters. In: O’Leary, D. (ed.): Proceedings of the International Linear Algebra Society Symposium on Fast Algorithms for Control, Signals and Image Processing. Elsevier, Amsterdam (1998) 125–135

    Google Scholar 

  16. Cook, R.D.: Detection of Influential Observations in Linear Regression. Technometrics 19 (1977) 15–18

    Article  MATH  MathSciNet  Google Scholar 

  17. Peña, D.: Influential Observations in Time Series. J. Business & Economic Statistics 8 (1990) 235–241

    Article  Google Scholar 

  18. De Jong, P., Penzer, J.: Diagnosing Shocks in Time Series. J. Americ. Statist. Assoc. 93 (1998) 796–806

    Article  MATH  Google Scholar 

  19. Gather, U., Fried, R., Imhoff, M.: Online Classification of States in Intensive Care. Technical Report 15/2000, SFB 475, University of Dortmund, 44221 Dortmund, Germany.

    Google Scholar 

  20. Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis. Forecasting and Control. Third Edition. Prentice Hall, Englewood Cliffs (1994)

    Google Scholar 

  21. Imhoff, M., Bauer, M., Gather, U., Fried, R.: Pattern Detection in Intensive Care Monitoring Time Series: Influence of the Model Order. Preprint, SFB 475, University of Dortmund, 44221 Dortmund, Germany

    Google Scholar 

  22. De Gooijer, J.G., Abraham, B., Gould, A., Robinson, L.: Methods for Determining the Order of an Autoregressive-moving Average Process: a Survey. Int. Stat. Rev. 55 (1985) 301–329

    Google Scholar 

  23. Imhoff, M., Bauer, M.: Time Series Analysis in Critical Care Monitoring. New Horizons 4 (1996) 519–531

    Google Scholar 

  24. Lambert, C.R., Raymenants, E., Pepine, C.J.: Time-Series Analysis of Long-Term Ambulatory Myocardial Ischemia: Effects of Beta-Adrenergic and Calcium Channel Blockade. Am. Heart J. 129 (1995) 677–684

    Article  Google Scholar 

  25. Bauer, M., Gather, U., Imhoff, M.: The Identification of Multiple Outliers in Online Monitoring Data. Technical Report 29/1999, SFB 475, University of Dortmund, 44221 Dortmund, Germany

    Google Scholar 

  26. Bauer, M., Gather, U., Imhoff, M.: Analysis of High Dimensional Data from Intensive Care Medicine. In: Payne, R., Green, P. (eds.): Proceedings in Computational Statistics. Springer-Verlag, Berlin (1999) 185–190

    Google Scholar 

  27. Cox, D.R., Wermuth, N.: Multivariate Dependencies. Chapman & Hall, London (1996)

    MATH  Google Scholar 

  28. Dahlhaus, R.: Graphical Interaction Models for Multivariate Time Series. Metrika (2000) to appear

    Google Scholar 

  29. Gather, U., Imhoff, M., Fried, R.: Graphical Models for Multivariate Time Series from Intensive Care Monitoring. Preprint, SFB 475, University of Dortmund, 44221 Dortmund, Germany

    Google Scholar 

  30. Li, K.C.: Sliced Inverse Regression for Dimension Reduction. J. Americ. Statist. Asoc. 86 (1991) 316–342

    Article  MATH  Google Scholar 

  31. Morik, K., Imhoff, M., Brockhausen, P., Joachims, T., Gather, U.: Knowledge Discovery and Knowledge Validation in Intensive Care. Art. Int. Med. (2000) to appear

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Dortmund, Vogelpothsweg 87, D-44221, Dortmund, Germany

    Roland Fried, Ursula Gather & Marcus Bauer

  2. Surgical Department, Community Hospital Dortmund, Beurhausstr. 40, D-44137, Dortmund, Germany

    Michael Imhoff

Authors
  1. Roland Fried
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  2. Ursula Gather
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  3. Michael Imhoff
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  4. Marcus Bauer
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Editor information

Editors and Affiliations

  1. Fachbereich Biologie und Informatik AG Adaptive Systemarchitektur, Johann Wolfgang Goethe-Universität, Robert-Mayer-Str. 11-15, 60054, Frankfurt a.M., Germany

    Rüdiger W. Brause

  2. Klinik für Allgemein- und Gefässchirurgie, Johann Wolfgang Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt a.M., Germany

    Ernst Hanisch

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© 2000 Springer-Verlag Berlin Heidelberg

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Fried, R., Gather, U., Imhoff, M., Bauer, M. (2000). Some Statistical Methods in Intensive Care Online Monitoring — A Review. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_11

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  • DOI: https://doi.org/10.1007/3-540-39949-6_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41089-8

  • Online ISBN: 978-3-540-39949-0

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