Online Modeling and Monitoring of Dependent Processes under Resource Constraints

TL;DR

This paper introduces a novel online collaborative learning method for adaptive monitoring of dependent processes, effectively balancing resource constraints, uncertainty reduction, and process health assessment in healthcare and engineering systems.

Contribution

It proposes the CL-UCB algorithm that accounts for process dependencies and uncertainty, advancing adaptive monitoring strategies under resource limitations.

Findings

The CL-UCB algorithm effectively balances exploration and exploitation.

The method is theoretically sound and empirically validated.

Application to Alzheimer's disease monitoring demonstrates practical utility.

Abstract

Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources in many healthcare and engineering systems. Examples include the risk-based disease screening and condition-based process monitoring. However, existing adaptive monitoring models either ignore the dependency among processes or overlook the uncertainty in process modeling. To design an optimal monitoring strategy that accurately monitors the processes 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 (CL-UCB) algorithm to optimally balance the exploitation and exploration of dependent processes 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.