Multi-task Gaussian process models for biomedical applications

Type of publication:  Artikel in einem Konferenzbericht
Buchtitel: Proceedings of the International Conference on Biomedical and Health Informatics (IEEE BHI)
Jahr: 2014
Monat: Juni
Seiten: 492-495
Ort: Valencia, Spain
Notiz: Motion Compensation in Radiosurgery
DOI: 10.1109/BHI.2014.6864410
Abriss: Gaussian process (GP) models are a flexible means of performing non-parametric Bayesian regression. However, the majority of existing work using GP models in healthcare data is defined for univariate output time-series, denoted as single-task GPs (STGP). Here, we investigate how GPs could be used to model multiple correlated univariate physiological time-series simultaneously. The resulting multi-task GP (MTGP) framework can learn the correlation within multiple signals even though they might be sampled at different frequencies and have training sets available for different intervals. We illustrate the basic properties of MTGPs using a synthetic casestudy with respiratory motion data. Finally, two real-world biomedical problems are investigated from the field of patient monitoring and motion compensation in radiotherapy. The results are compared to STGPs and other standard methods in the respective fields. In both cases, MTGPs learned the correlation between physiological time-series efficiently, which leads to improved modelling accuracy.
Autoren: Dürichen, Robert
Pimentel, Marco A. F.
Clifton, Lei
Schweikard, Achim
Clifton, David A.
  • 2014 -BHI - Dürichen - Multi-t...