  False alarm is one of the main concerns in intensive care units and can
result in care disruption, sleep deprivation, and insensitivity of care-givers
to alarms. Several methods have been proposed to suppress the false alarm rate
through improving the quality of physiological signals by filtering, and
developing more accurate sensors. However, significant intrinsic correlation
among the extracted features limits the performance of most currently available
data mining techniques, as they often discard the predictors with low
individual impact that may potentially have strong discriminatory power when
grouped with others. We propose a model based on coalition game theory that
considers the inter-features dependencies in determining the salient predictors
in respect to false alarm, which results in improved classification accuracy.
The superior performance of this method compared to current methods is shown in
simulation results using PhysionNet's MIMIC II database.
