Abstract
In this paper we present a new approach for the intelligent analysis of longitudinal data coming from diabetic patients home monitoring. This approach consists in exploiting temporal abstractions to pre-process the raw data and to obtain a new time series of abstract episodes, whose features are then interpreted through statistical and probabilistic techniques. We finally show the application of this methodology on the data of two diabetic patients monitored for six months.
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
Kahn, M.G., Abrams, C.A., Orland, M.J., Beard, J.C., Cousin, S.B., Miller, J.P., Santiago, J.V.: Intelligent computer-based interpretation and graphical presentation of self-monitored blood glucose and insulin data. Diab. Nutr. Metab. 4 (1991) 99–107.
Shahar, Y., Musen, M.A.: Knowledge-Based Temporal Abstraction in Clinical Domains. Artificial Intelligence in Medicine 8 (1996) 267–298
Miksch, S., Horn, W., Popow, C., Paky, F.: Therapy Planning Using Qualitative Trend Descriptions. Lecture Notes in Artificial Intelligence, P. Barahona, M. Stefanelli and J. Wyatt eds., Springer Verlag, Berlin (1995) 197–208
The Diabetes Control and Complication Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. The New England Journal of Medicine 14–329 (1993) 977–986
Deutsch, T., Lehmann, E.D., Carson, E.R., Roudsari, A.V., Hopkins, K.D., Sönksen, P.: Time series analysis and control of blood glucose levels in diabetic patients. Comp. Meth. and Programs in Biomed. 41 (1994) 167–182
Andreassen, S., Benn, J., Hovorka, R., Olesen, K.G., Carson, E.R.: A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. Computer Methods and Programs in Biomedicine 41 (1994) 153–165
Allen, J. F.: Towards a general theory of action and time. Artificial Intelligence 23 (1984) 123–154
Larizza, C, Bernuzzi, G., Stefanelli, M.: A General Framework for Building Patient Monitoring Systems. Lecture Notes in Artificial Intelligence, P. Barahona, M. Stefanelfi and J. Wyatt eds., Springer Verlag, Berlin (1995) 91–102
Ramoni, M., and Sebastiani, P.: Robust Learning with missing data. Knowledge Media Institute Report, The Open University, KMI-TR-28 (1996)
Riva, A., Bellazzi, R.: Intelligent Analysis Techniques for Diabetes Data Time Series. In: Advances in Intelligent Data Analysis, G. Lasker and X. Liu eds., IIAS Press, Germany (1995) 144–148
Riva, A., Bellazzi, R.: Learning Temporal Probabilistic Causal Models from Longitudinal Data. Artificial Intelligence in Medicine 8 (1996) 217–234
Bellazzi, R., Larizza, C, Riva, A.: Temporal abstractions for pre-processing and interpreting diabetes monitoring time series, in: Intelligent Data Analysis in Medicine and Pharmacology, IJCAI workshop, Nagoya, Japan (1997)
Advances in Knowledge Discovery and Data Mining. U. Fayad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy eds., AAAI Press, Menlo Park, California (1996)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression Trees. Wadsworth, California, 1984
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag
About this paper
Cite this paper
Bellazzi, R., Larizza, C., Riva, A. (1997). Interpreting longitudinal data through temporal abstractions: An application to diabetic patients monitoring. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052848
Download citation
DOI: https://doi.org/10.1007/BFb0052848
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63346-4
Online ISBN: 978-3-540-69520-2
eBook Packages: Springer Book Archive