An Integrated Approach for Healthcare Planning over Multi-dimensional Data Using Long-Term Prediction

  • Rui Henriques
  • Cláudia Antunes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7231)


The mining of temporal aspects over multi-dimensional data is increasingly critical for healthcare planning tasks. A healthcare planning task is, in essence, a classification problem over health-related attributes across temporal horizons. The increasingly integration of healthcare data through multi-dimensional structures triggers new opportunities for an adequate long-term planning of resources within and among clinical, pharmaceutical, laboratorial, insurance and e-health providers. However, the flexible nature and random occurrence of health records claim for the ability to deal with both structural attribute-multiplicity and arbitrarily-high temporal sparsity. For this purpose, two solutions using different structural mappings are proposed: an adapted multi-label classifier over denormalized tabular data and an adapted multiple time-point classifier over multivariate sparse time sequences. This work motivates the problem of long-term prediction in healthcare, and places key requirements and principles for its accurate and efficient solution.


Health Record Multivariate Adaptive Regression Spline Inductive Logic Programming Normalize Root Mean Square Error Time Series Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rui Henriques
    • 1
  • Cláudia Antunes
    • 1
  1. 1.D2PM, IST–UTLPortugal

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