Finding temporal patterns in noisy longitudinal data: a study in diabetic retinopathy

Authors:
Vassiliki Somaraki;Deborah Broadbent;Frans Coenen;Simon Harding
Affiliations:
Dept. of Computer Science, The University of Liverpool, Liverpool, UK and Ophthamology Research Unit, School of Clinical Science, The University of Liverpool, Liverpool, UK;Ophthamology Research Unit, School of Clinical Science, The University of Liverpool, Liverpool, UK and St. Pauls Eye Unit, Royal Liverpool University Hospital, UK;Dept. of Computer Science, The University of Liverpool, Liverpool, UK and St. Pauls Eye Unit, Royal Liverpool University Hospital, UK;Ophthamology Research Unit, School of Clinical Science, The University of Liverpool, Liverpool, UK and St. Pauls Eye Unit, Royal Liverpool University Hospital, UK
Venue:
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Year:
2010

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Abstract

This paper describes an approach to temporal pattern mining using the concept of user defined temporal prototypes to define the nature of the trends of interests. The temporal patterns are defined in terms of sequences of support values associated with identified frequent patterns. The prototypes are defined mathematically so that they can be mapped onto the temporal patterns. The focus for the advocated temporal pattern mining process is a large longitudinal patient database collected as part of a diabetic retinopathy screening programme, The data set is, in itself, also of interest as it is very noisy (in common with other similar medical datasets) and does not feature a clear association between specific time stamps and subsets of the data. The diabetic retinopathy application, the data warehousing and cleaning process, and the frequent pattern mining procedure (together with the application of the prototype concept) are all described in the paper. An evaluation of the frequent pattern mining process is also presented.