NDLI: Temporal pattern discovery for trends and transient effects: its application to patient records

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Temporal pattern discovery for trends and transient effects: its application to patient records

Content Provider	ACM Digital Library
Author	Star, Kristina Norén, G. Niklas Hopstadius, Johan Bate, Andrew Edwards, I. Ralph
Abstract	We introduce a novel pattern discovery methodology for event history data focusing explicitly on the detailed temporal relationship between pairs of events. At the core is a graphical statistical approach to summarising and visualising event history data, which contrasts the observed to the expected incidence of the event of interest before and after an index event. Thus, pattern discovery is not restricted to a specific time window of interest, but encompasses extended parts of the underlying event histories. In order to effectively screen large collections of event history data for interesting temporal relationships, we introduce a new measure of temporal association. The proposed measure contrasts the observed-to-expected ratio in a time period of interest to that in a pre-defined control period. An important feature of both the observed-to-expected graph itself and the measure of association, is a statistical shrinkage towards the null hypothesis of no association. This provides protection against spurious associations and is an extension of the statistical shrinkage successfully applied to large-scale screening for associations between events in cross-sectional data, such as large collections of adverse drug reaction reports. We demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the United Kingdom. The identified patterns include temporal relationships between drug prescription and medical events suggestive of persistent or transient risks of adverse events, as well as temporal relationships between prescriptions of different drugs.
Starting Page	963
Ending Page	971
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1402005
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Patient records Temporal pattern discovery Event history data
Content Type	Text
Resource Type	Article

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