  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.
