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Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation.

Earl W DuncanNicole M WhiteKerrie Mengersen
Published in: BMJ open (2016)
This paper demonstrates the usefulness of the two models in identifying unusual and unstable temporal trends, and the synergy obtained when both models are applied to the same data set. An analysis of these models has provided interesting insights into the temporal trends of mammography screening counts and has shown several possible avenues for further research, such as extending the models to allow for multiple common temporal trends and accounting for additional spatiotemporal heterogeneity.
Keyphrases
  • contrast enhanced
  • magnetic resonance imaging
  • single cell
  • electronic health record
  • computed tomography
  • magnetic resonance
  • machine learning
  • image quality
  • peripheral blood
  • artificial intelligence