Length of stay-based clustering methods for patient grouping

El-Darzi, E., Abbi, R., Vasilakis, C., Gorunescu, F., Gorunescu, M. and Millard, P.H. 2009. Length of stay-based clustering methods for patient grouping. in: McClean, S.I., Millard, P.H., El-Darzi, E. and Nugent, C. (ed.) Intelligent patient management Berlin Heidelberg Springer.

Chapter titleLength of stay-based clustering methods for patient grouping
AuthorsEl-Darzi, E., Abbi, R., Vasilakis, C., Gorunescu, F., Gorunescu, M. and Millard, P.H.
EditorsMcClean, S.I., Millard, P.H., El-Darzi, E. and Nugent, C.
Abstract

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.

Book titleIntelligent patient management
Year2009
PublisherSpringer
Publication dates
Published2009
Place of publicationBerlin Heidelberg
SeriesStudies in computational intelligence
ISBN9783642001789
Digital Object Identifier (DOI)doi:10.1007/978-3-642-00179-6_3
Journal citation189 (189), pp. 39-56

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