El-Darzi, Elia and Abbi, Revlin and Vasilakis, Christos and Gorunescu, Florin and Gorunescu, Marina and Millard, Peter H. (2009) Length of stay-based clustering methods for patient grouping. In: McClean, Sally I. and Millard, Peter H. and El-Darzi, Elia and Nugent, Chris, (eds.) Intelligent patient management. Studies in computational intelligence, 189 (189). Springer, Berlin Heidelberg, pp. 39-56. ISBN 9783642001789
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Official URL: http://dx.doi.org/10.1007/978-3-642-00179-6_3
Length of stay (LOS) is often used as a proxy measure of a patient’ resource consumption because of the practical difficulties of directly measuring resource consumption and the easiness of calculating LOS. Grouping patient spells according to their LOS has proved to be a challenge in health care applications due to the inherent variability in the LOS distribution. Sound methods for LOS-based patient grouping should certainly lead to a better planning of bed allocation, and patient admission and discharge. Grouping patient spells according to their LOS in a computational efficient manner is still a research issue that has not been fully addressed. For instance, grouping patient spells according to LOS intervals (e.g. 0-3 days, 4-9 days, 10-21 days etc.), has previously been defined by non-algorithmic approaches using clinical judgement, visual inspection of the LOS distribution or according to the perceived casemix. The aim of this paper is to present a novel methodology of grouping patients according to their length of stay based on fitting Gaussian mixture models to LOS observations. This method was developed as part of an innovative prediction tool that helps identify groups of patients exhibiting similar resource consumption levels as these are approximated by patient LOS. As part of evaluating the approach, we also compare it to two alternative clustering approaches, K-means and the two-step algorithm. Computational results show the superiority of this method compared to alternative clustering approaches in terms of its ability to extract clinically meaningful patient groups as applied to a skewed LOS dataset.
|Item Type:||Book Section|
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||10 Nov 2010 15:46|
|Last Modified:||19 Oct 2011 10:16|
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