Abstract
Many machines used in the modern hospital settings offer real time physiological monitoring. Haemodialysis machines combine a therapeutic treatment system integrated with sophisticated monitoring equipment. A large array of parameters can be collected including cardiovascular measures such as heart rate and blood pressure together with treatment related data including relative blood volume, ultrafiltration rate and small molecule clearance. A small subset of this information is used by clinicians to monitor treatment and plan therapeutic strategies but it is not usually analysed in any detail. The focus of this paper is the analysis of data collected over a number of treatment sessions with a view to predicting patient physiological behaviour whilst on dialysis and correlating this with clinical characteristics of individual patients.
One of the commonest complications experienced by patients on dialysis is symptomatic hypotension. We have taken real time treatment data and outline a program of work which attempts to predict when hypotension is likely to occur, and which patients might be particularly prone to haemodynamic instability. This initial study has investigated: the rate of change of blood pressure versus rate of change of heart rate, rate of fluid removal, and rate of uraemic toxin clearance. We have used a variety of machine learning techniques (including hierarchical clustering, and Bayesian Network analysis algorithms). We have been able to detect from this dataset, 3 distinct groups which appear to be clinically meaningful. Furthermore we have investigated whether it is possible to predict changes in blood pressure in terms of other parameters with some encouraging results that merit further study.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Craw, S., Sleeman, D.: Automating the Refinement of Knowledge-Based Systems. In: Aiello, L. (ed.) Proceedings of ECCAI-1990, pp. 167–172. Pitman, London (1990)
Daugirdas, J.T., Blake, P.G., Ing, T.S.: Handbook of Dialysis, 3rd edn. Lippincott, Williams, & Wilkins, Baltimore (2001)
Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)
Howells, T.P.: Edinburgh Monitor-Browser© Software, [v1.0.0] Computer Program (1994) http://tim.howellsnc.uas.lul.se
McQuatt, A., Andrews, P.J.D., Sleeman, D., Corruble, V., Jones, P.A.: The analyses of Head Injury data using Decision Tree techniques. Artificial Intelligence in Medicine. In: Horn, W., et al. (eds.) Proceedings of AIMDM 1999 Conference, Aalborg, Denmark, June 1999, pp. 336–345. Springer, Heidelberg (1999)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, vol. 1, pp. 281–297. University of California Press (1967)
Sleeman, D., Luo, Z., Christie, G., Coghill, G.: Analysing Time Series Medical Data-sets. In: Proceedings of Knowledge Based Systems & Services for Health Care, Bonn, May 2004, p. 1–4 (2004)
Ward, J.H.: Hierarchical Grouping to optimize an objective function. Journal of American Statistical Association 58(301), 236–244 (1963)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sleeman, D., Fluck, N., Gyftodimos, E., Moss, L., Christie, G. (2007). An Intelligent Aide for Interpreting a Patient’s Dialysis Data Set. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-73599-1_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
eBook Packages: Computer ScienceComputer Science (R0)