Personalised patient monitoring in haemodialysis using hierarchical Gaussian processes
The prevalence of end-stage renal failure is 861 per million population in the UK, and these patients undergo three haemodialysis sessions per week, each lasting 4 hours. In addition, patients are at risk of intra-dialytic hypotension, which leads to chronic heart disease and a high incidence of mor...
Huvudupphovsmän: | Zhu, T, Colopy, G, Pugh, C, Clifton, D |
---|---|
Materialtyp: | Conference item |
Publicerad: |
Institute of Electrical and Electronics Engineers
2017
|
Liknande verk
Liknande verk
-
Patient-specific physiological monitoring and prediction using structured Gaussian processes
av: Zhu, T, et al.
Publicerad: (2019) -
Bayesian optimisation of personalised models for patient vital-sign monitoring
av: Colopy, GW, et al.
Publicerad: (2017) -
Bayesian Gaussian processes for identifying the deteriorating patient
av: Colopy, G, et al.
Publicerad: (2016) -
State-space approximations to Gaussian processes for patient vital-sign monitoring in computationally-constrained clinical environments
av: Colopy, G, et al.
Publicerad: (2016) -
Bayesian optimisation of Gaussian processes for identifying the deteriorating patient
av: Colopy, G, et al.
Publicerad: (2017)