By Topic

Using Markov Models to Find Interesting Patient Pathways

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
McClean, S. ; Univ. of Ulster, Derry ; Garg, L. ; Meenan, B. ; Millard, P.

Over recent years the concept of Interestingness has come to underpin Data Mining, leading to the discovery of much new knowledge. In particular recognition of interesting patient pathways can lead to the discovery of important rules and patterns such as high probability pathways, groups of patients who incur exceptional high costs or pathways that are very long lasting. In the current paper we show how Markov models can be used to identify such patient pathways. Using Markov modelling we show how patient pathways may be extracted and describe an algorithm based on branch and bound that we have developed to efficiently extract a number of interesting pathways, subject to the number of pathways required, or some other criterion being specified. The approach is illustrated using data on geriatric patients from an administrative database of a London hospital, and we identify interesting pathways for geriatric patients. Such an approach might be used in association with healthcare process improvement technologies, such as Lean Thinking or Six Sigma.

Published in:

Computer-Based Medical Systems, 2007. CBMS '07. Twentieth IEEE International Symposium on

Date of Conference:

20-22 June 2007