Model Exploration of an Information-Based Healthcare Intervention Using Parallelization and Active Learning

This paper describes the application of a large-scale active learning method to characterize the parameter space of a computational agent-based model developed to investigate the impact of CommunityRx, a clinical information-based health intervention that provides patients with personalized informat...

Full description

Saved in:
Bibliographic Details
Published in:Journal of artificial societies and social simulation 2020-10, Vol.23 (4)
Main Authors: Kaligotla, Chaitanya, Ozik, Jonathan, Collier, Nicholson, Macal, Charles M., Boyd, Kelly, Makelarski, Jennifer, Huang, Elbert S., Lindau, Stacy T.
Format: Article
Language:English
Subjects:
Active learning
Agent-based modeling
Clinical information
Community
Community resources
Experiments
Feasibility
Health care
Health services
High performance computing
Information dissemination
Intervention
Learning
Mathematical models
MATHEMATICS AND COMPUTING
Model exploration
Public access
Self care
Simulation
Urban population
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper describes the application of a large-scale active learning method to characterize the parameter space of 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 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; by partitioning the parameter space into potentially viable and non-viable regions, we rule out regions of space where simulation output is implausible to observed empirical data. We argue that such methods are necessary to enable model exploration in complex computational models that incorporate increasingly available micro-level behavior data. We provide public access to the model and high-performance computing experimentation code.
ISSN:1460-7425
1460-7425
DOI:10.18564/jasss.4379