Model Exploration of an Information-based Healthcare Intervention using Parallelization and Active Learning

Abstract

This paper describes the application of a large-scale active learning method to calibrate a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized information about local community resources to meet basic and self-care needs. The diffusion of information about community resources and their use, is modeled via networked interactions and their subsequent effect on agents' use of community resources across an urban population. A random forest model is iteratively fitted to model evaluations in order to characterize the model parameter space with respect to observed empirical data. We demonstrate the feasibility of using high-performance computing and active learning model exploration techniques to characterize large parameter spaces and argue that such methods are necessary to build trust in complex computational models that incorporate increasingly available micro-level behavior data.