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INCDM
2010
Springer

Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy

11 years 1 months ago
Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy
This paper describes an approach to temporal pattern mining using the concept of user de ned temporal prototypes to de ne the nature of the trends of interests. The temporal patterns are de ned in terms of sequences of support values associated with identi ed frequent patterns. The prototypes are de ned 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 speci c 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 frequ...
Vassiliki Somaraki, Deborah Broadbent, Frans Coene
Added 12 Oct 2010
Updated 12 Oct 2010
Type Conference
Year 2010
Where INCDM
Authors Vassiliki Somaraki, Deborah Broadbent, Frans Coenen, Simon Harding
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