Abstract
On-line monitoring at neonatal intensive care units produces high volumes of data. Numerous devices generate data at high frequency (one data set every second). Both, the high volume and the quite high error-rate of the data make it essential to reach at higher levels of description from such raw data. These abstractions should improve the medical decision making. We will present a time-oriented data-abstraction method to derive steady qualitative descriptions from oscillating high-frequency data. The method contains tunable parameters to guide the sensibility of the abstraction process. The benefits and limitations of the different parameter settings will be discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R.K. Avent and J.D. Charlton. A critical review of trend-detection methologies for biomedical monitoring systems. Critical Reviews in Biomedical Engineering, 17(6):621–659, 1990.
I. J. Haimowitz and I. S. Kohane. Managing temporal worlds for medical tread diagnosis. Artificial Intelligence in Medicine, Special Issue Temporal Reasoning in Medicine, 8(3):299–321, 1996.
W. Horn, S. Miksch, G. Egghart, C. Popow, and F. Paky. Effective data validation of high-frequency data: Time-point-, time-interval-, and trend-based methods. Computer in Biology and Medicine, Special Issue: Time-Oriented Systems in Medicine, 27(5):389–409, 1997.
J. Hunter. Knowledge-based interpretation of time series data from the neonatal ICU. presentation, 1998.
Goldsmith J.P. and Karotkin E.H. Assisted Ventilation of the Neonates. Saunders, Philadelphia, 1996.
E. T. Keravnou. Temporal abstraction of medical data: Deriving periodicity. In N. Lavrac, et. al., editors, Intelligent Data Analysis in Medicine and Pharmacology, pages 61–79. Kluwer Academic Publisher, Boston, 1997.
C. Larizza, R. Bellazzi, and A. Riva. Temporal abstractions for diabetic patients management. In Proceedings of the Artificial Intelligence in Medicine, 6th Conference on Artificial Intelligence in Medicine Europe (AIME-97), pages 319–30, Berlin, 1997. Springer.
S. Miksch, W. Horn, C. Popow, and F. Paky. Utilizing temporal data abstraction for data validation and therapy planning for artificially ventilated newborn infants. Artificial Intelligence in Medicine, 8(6):543–576, 1996.
W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery. Numerical Recipies in C. Cambridge University Press, Cambridge, 1992.
Y. Shahar, S. Miksch, and P. Johnson. The Asgaard Project: A task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artificial Intelligence in Medicine, 14:29–51, 1998.
Y. Shahar and M. A. Musen. Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine, Special Issue Temporal Reasoning in Medicine, 8(3):267–98, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miksch, S., Seyfang, A., Horn, W., Popow, C. (1999). Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J. (eds) Artificial Intelligence in Medicine. AIMDM 1999. Lecture Notes in Computer Science(), vol 1620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48720-4_31
Download citation
DOI: https://doi.org/10.1007/3-540-48720-4_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66162-7
Online ISBN: 978-3-540-48720-3
eBook Packages: Springer Book Archive