Online Modeling and Monitoring for Dependent Dynamic Processes Under Resource Constraints

Adaptive monitoring of a large population of dynamic units is critical for the timely detection of abnormal events under limited resources in many healthcare and engineering systems. Examples include risk-based disease screening and condition-based unit monitoring. However, existing adaptive monitoring models either ignore the dependency among units or overlook the uncertainty in unit modeling. To design an optimal monitoring strategy that accurately monitors the units with poor health conditions and actively collects information for uncertainty reduction, a novel online collaborative learning method is proposed in this study. The proposed method designs a collaborative learning–based upper confidence bound algorithm to optimally balance the exploitation and exploration of dependent units under limited resources. Efficiency of the proposed method is demonstrated through theoretical analysis, simulation studies, and an empirical study of adaptive cognitive monitoring in Alzheimer’s disease.

History: Kwok-Leung Tsui served as the senior editor for this article.

Funding: T. Kosolwattana and Y. Lin are supported by Department of Education [Grant P116S230007]. H. Wang is supported by National Science Foundation [Grant IIS-2403401].

Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.2608199.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0021).