Bayesian hierarchical vector autoregressive models for patient-level predictive modeling.

ABSTRACT: Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling. In this paper, we propose a Bayesian hierarchical vector autoregressive (VAR) model to predict medical and psychological conditions using multivariate time series data. Compared to the existing patient-specific predictive VAR models, our model demonstrated higher accuracy in predicting future observations in terms of both point and interval estimates due to the pooling effect of the hierarchical model specification. In addition, by adopting an elastic-net prior, our model offers greater interpretability about the associations between variables of interest on bot

SUBMITTER: Lu F 

PROVIDER: S-EPMC6294362 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature