A feature representation learning method for temporal datasets

@article{Breda2016AFR,
  title={A feature representation learning method for temporal datasets},
  author={Ward R. J. van Breda and M. Hoogendoorn and A. E. Eiben and G. Andersson and H. Riper and J. Ruwaard and K. Vernmark},
  journal={2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
  year={2016},
  pages={1-8}
}
  • Ward R. J. van Breda, M. Hoogendoorn, +4 authors K. Vernmark
  • Published 2016
  • Computer Science
  • 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
  • Predictive modeling of future health states can greatly contribute to more effective health care. Healthcare professionals can for example act in a more proactive way or predictions can drive more automated ways of therapy. However, the task is very challenging. Future developments likely depend on observations in the (recent) past, but how can we capture this history in features to generate accurate predictive models? And what length of history should we consider? We propose a framework that… CONTINUE READING
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