Williams, GrahamBaxter, RohanKelman, ChrisRainsford, ChrisHe, HongxingGu, LifangVickers, DeanneHawkins, Simon2025-03-232025-03-23354000197297835400019730302-9743Scopus:84930164224https://dspace-test.anu.edu.au/handle/1885/733732014Australia has extensive administrative health data collected by Commonwealth and state agencies. Using a unique cleaned and linked administrative health dataset we address the problem of empirically defining episodes of care. An episode of care is a time interval containing medical services relating to a particular medical situation. In this paper the medical situation is a hospital admission. The medical services of interest are pathology tests, diagnostic imaging and non-invasive investigative procedures performed before or after the hospital admission, but 'associated' with the hospital admission. The task can be viewed as detecting a signal in a time series relating to a hospital admission, distinct from the background noise of on-going medical care. Our approach uses an ensemble (panel of experts) paradigm where we implement multiple agents (alternative predictive models) to separately estimate intervals and then choose a robust interval estimate using a voting scheme. The results have been used in a study for the Commonwealth Department of Health and Ageing.12EnglishPublisher Copyright: © Springer-Verlag Berlin Heidelberg 2002.Administrative dataApplicationsHealth servicesKnowledge discovery and data miningMachine learningRecord linkage200210.1007/3-540-36187-1_58http://www.scopus.com/inward/record.url?scp=84930164224&partnerID=8YFLogxK