Skip Navigation



IMA Journal of Management Mathematics Advance Access published online on November 20, 2008
IMA Journal of Management Mathematics, doi:10.1093/imaman/dpn030
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
20/4/327    most recent
dpn030v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Garg, L.
Right arrow Articles by Millard, P.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The authors 2008. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Non-homogeneous Markov models for sequential pattern mining of healthcare data

Lalit Garg{dagger} and Sally McClean{ddagger}

School of Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, BT52 1SA, UK

Brian Meenan§

School of Engineering, University of Ulster, Jordanstown Campus, Newtownabbey, Co. Antrim, BT37 0QB, UK

Peter Millard

St. George's Hospital Medical School, 12 Cornwall Road, Cheam, Sutton, Surrey SM2 6DR, UK

{dagger} Corresponding author. Email: garg-l{at}ulster.ac.uk

{ddagger} Email: si.mcclean{at}ulster.ac.uk

§ Email: bj.meenan{at}ulster.ac.uk

Email: phmillard{at}tiscali.co.uk

Received on 1 May 2007. Accepted on 1 May 2008.

Sequential pattern mining has been a popular data mining technique for extracting useful information from large databases and has successfully been used for numerous industrial and commercial problems. This paper presents a new mathematical modelling application to healthcare, providing important information to health service managers and policy makers to help them identify sequential patterns which require attention for efficiently managing scarce healthcare resources and developing effective healthcare management policies. In healthcare, these sequential patterns are analogous to the patient pathways. We present a non-homogeneous Markov model for identifying not only patient pathways which have high probability but also for identifying pathways which incur high cost or time. In order to have a more realistic model, we also consider time-dependent covariates and their impact on the pathways. An algorithm based on branch and bound global optimization is presented which can efficiently extract a required number of such patient pathways of interest. The approach is illustrated using historical data on geriatric patients from an administrative database of a London hospital.

Keywords: sequential patterns; healthcare modelling; interesting patterns; non-homogeneous Markov models; patient pathways; sequential pattern mining


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.