A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU and Sparse, Heterogeneous Clinical Data
We evaluate the use of multivariate timeseries modeling with the multi-task Gaussian process (GP) models using noisy, incomplete, sparse, heterogeneous and unevenlysampled clinical data, including both physiological signals and clinical notes. The learned multi-task GP (MTGP) hyperparameters are then used to assess and forecast patient acuity.
Presenter: Tristan Naumann
Paper: ghassemi_AAAI2015_multivariate_timeseries_modeling
Poster: MIT_AAAI_MTGP_lg