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To project disease progression, a Markov model was built based on clinical stages of chronic disease.
The calibration curve for clinical stage illustrates how narrow the band of predicted probabilities is for the model built based on clinical stage (DRE) alone.
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The model was built based on the training set with known clinical status to construct a set of linear functions of the measured metabolites.
The models were built based on a stepwise selection method as well as expert clinical guidance and the models fit were tested using the AIC.
Models were built based on real topography.
WTIF, SORCRADES, and RESTful BPM are built based on BPMS.
All the three models were built based on Morgan fingerprint.
A Bayes kernel was built based on this concept.
DARTCSIM [16] was also built based on CloudSim.
Each segment is built based on its preceding segment.
The map is built based on particle positions.
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