Risk prediction for heterogeneous populations with application to hospital admission prediction.

ABSTRACT: This article is motivated by the increasing need to model risk for large hospital and health care systems that provide services to diverse and complex patients. Often, heterogeneity across a population is determined by a set of factors such as chronic conditions. When these stratifying factors result in overlapping subpopulations, it is likely that the covariate effects for the overlapping groups have some similarity. We exploit this similarity by imposing structural constraints on the importance of variables in predicting outcomes such as hospital admission. Our basic assumption is that if a variable is important for a subpopulation with one of the chronic conditions, then it should be important for the subpopulation with both conditions. However, a variable can be important for the subpo

SUBMITTER: Huling JD 

PROVIDER: S-EPMC6076875 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature