Learning compressed representations of blood samples time series with missing data

@article{Bianchi2017LearningCR,
  title={Learning compressed representations of blood samples time series with missing data},
  author={Filippo Maria Bianchi and Karl {\O}yvind Mikalsen and Robert Jenssen},
  journal={ArXiv},
  year={2017},
  volume={abs/1710.07547}
}
Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data. An autoencoder can learn low dimensional vectorial representations of MTS that preserve important data characteristics, but cannot deal explicitly with missing data. In this work, we propose a new framework that combines an autoencoder with the Time series Cluster Kernel (TCK), a kernel that accounts for missingness patterns in MTS. Via kernel alignment, we… CONTINUE READING
6
Twitter Mentions

Figures, Tables, and Topics from this paper.

Explore Further: Topics Discussed in This Paper

References

Publications referenced by this paper.
SHOWING 1-10 OF 11 REFERENCES

Time Series Feature Learning with Applications to Health Care

  • Mobile Health - Sensors, Analytic Methods, and Applications
  • 2017
VIEW 1 EXCERPT

Learning deep architectures for AI

Y. Bengio
  • Foundations and trends in Machine Learning,
  • 2009
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…