Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data.

ABSTRACT: Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically

SUBMITTER: Liu Z 

PROVIDER: S-EPMC4980099 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature