Online Modeling and Monitoring of Dependent Processes under Resource Constraints
This addresses the need for timely detection of abnormal events in healthcare and engineering systems, such as disease screening and process monitoring, by improving upon existing methods that ignore dependencies or uncertainty, though it appears incremental in its approach.
The paper tackles the problem of adaptively monitoring many dependent dynamic processes under resource constraints, proposing a novel online collaborative learning method that optimally balances exploitation and exploration, with efficiency demonstrated through theoretical analysis, simulations, and an empirical study on Alzheimer's disease monitoring.
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.