  Determining the optimal initial dose for warfarin is a critically important
task. Several factors have an impact on the therapeutic dose for individual
patients, such as patients' physical attributes (Age, Height, etc.), medication
profile, co-morbidities, and metabolic genotypes (CYP2C9 and VKORC1). These
wide range factors influencing therapeutic dose, create a complex environment
for clinicians to determine the optimal initial dose. Using a sample of 4,237
patients, we have proposed a companion classification model to one of the most
popular dosing algorithms (International Warfarin Pharmacogenetics Consortium
(IWPC) clinical model), which identifies the appropriate cohort of patients for
applying this model. The proposed model functions as a clinical decision
support system which assists clinicians in dosing. We have developed a
classification model using Support Vector Machines, with a polynomial kernel
function to determine if applying the dose prediction model is appropriate for
a given patient. The IWPC clinical model will only be used if the patient is
classified as "Safe for model". By using the proposed methodology, the dosing
mode's prediction accuracy increases by 15 percent in terms of Root Mean
Squared Error and 17 percent in terms of Mean Absolute Error in dose estimates
of patients classified as "Safe for model".
