In this setting, the ability to discover new medical knowledge is constrained by prior knowledge that has typically fallen short of maximally utilizing high-dimensional time series data.
Make a general survey of functional connectivity, the selected seed points analysis was based on the documents with some priori knowledge and constrained the degree of functional connectivity changes on the priori selection of specific seed regions, which would generate subjective bias in evaluating the intrinsic functional changes over the whole brain.
These showed no significant reduction in fit when the regression coefficients for attitude and knowledge were constrained to be the same in the two invitation groups (χ(2) = 4.83, p =.09), suggesting that the same model was applicable to the standard group as to the informed choice group.
AI-based approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.
The proposed approach exploits the clinical setup knowledge to constrain the search space while boosting both tracking speed and accuracy, and is based on a computationally efficient framework to trace the sheath and simultaneously detect one or multiple catheter tips.
Specifically, the authors focus on the reconstruction of K-compressible signals, e.g. signals that are approximately reconstructed by K coefficients, and show that if the signal presents a suitable structure, the reconstruction procedure can exploit this knowledge to constrain the recovered signal subspace and improve the accuracy for a given number of measurements.
In this work, we have presented two methods for incorporating additional knowledge to constrain GRN inference by adding priors on the network structure.
However, this is specific to this small and constrained knowledge base, and eliminating undesirable models by gradually adding axioms in this fashion is clearly a solution of little practical use, as it is domain-specific and tedious.
The choice of features may be influenced by prior biological knowledge and is constrained by statistical considerations and the available data.
In particular, systematic uncertainties are modelled by introducing nuisance parameters into the statistical model and allowing them to vary in the fit; those for which additional knowledge is present are constrained using Gaussian distributions.
