UNASSIGNED: Determining the appropriate dosage of @entity1229 is an important yet challenging task. Several prediction models have been proposed to estimate a therapeutic dose for @entity1 . The models are either clinical models which contain clinical and demographic variables or pharmacogenetic models which additionally contain the genetic variables. In this paper, a new methodology for @entity1229 dosing is proposed. The @entity1 are initially classified into two classes. The first class contains @entity1 who require doses of >30   mg/wk and the second class contains @entity1 who require doses of  <=30   mg/wk. This phase is performed using relevance vector machines. In the second phase, the optimal dose for each @entity1 is predicted by two clinical regression models that are customized for each class of @entity1 . The prediction accuracy of the model was 11.6 in terms of @entity2246 ( @entity2246 ) and 8.4 in terms of mean absolute error (MAE). This was 15% and 5% lower than IWPC and Gage models (which are the most widely used models in practice), respectively, in terms of @entity2246 . In addition, the proposed model was compared with fixed-dose approach of 35   mg/wk, and the model proposed by Sharabiani et al. and its outperformance were proved in terms of both MAE and @entity2246 .

Revisiting XXXX Dosing Using Machine Learning Techniques.

@entity1 :: ('9606', 'Species') :: ['patients', 'patient'] @entity1229 :: ('MESH:D014859', 'Chemical') :: ['warfarin'] @entity2246 :: ('MESH:D008661', 'Disease') :: ['RMSE', 'root mean squared error']
@entity1229 :: (MESH:D014859,Chemical) :: ['Warfarin']
