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

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 7231)

Abstract

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.

Keywords

  • 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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Henriques, R., Antunes, C. (2012). An Integrated Approach for Healthcare Planning over Multi-dimensional Data Using Long-Term Prediction. In: He, J., Liu, X., Krupinski, E.A., Xu, G. (eds) Health Information Science. HIS 2012. Lecture Notes in Computer Science, vol 7231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29361-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-29361-0_6

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