This method has advantages over prior means of assessing treatment thresholds.
This method has advantages over prior work in that it does not require physicians to estimate risk directly and allows them to be uncertain in their recommendations.
The low sensitivity of this method has been reported in prior studies from Thailand [17], United Kingdom [37], Denmark [10], and the United States [38].
Recently a novel shape prior modeling method has been proposed based on sparse learning theory.
The concerned method, also called the prior estimate method, has been used by many researchers of mathematics to study the existence of solutions to both nonlinear ordinary and nonlinear partial differential equations.
In prior benchmarking, the method has yielded affect intensity mean percentage errors of 5% or less on similar Dark Web forums [36].
This method has the advantage of not requiring prior knowledge about the signal under analysis except that its components are clearly separated in the time-frequency (TF) domain; a condition often satisfied by newborn EEG signals.
This new method has been fully described in a prior publication [ 6] and will be only briefly discussed here.
Besides, in order to minimize the modelling error on initial state, a prior-knowledge learning method has been proposed to get the initial parameters for control model estimation.
This method has been used and validated in numerous prior breast cancer studies [ 10- 12].
