  Warfarin dosing remains challenging due to narrow therapeutic index and
highly individual variability. Incorrect warfarin dosing is associated with
devastating adverse events. Remarkable efforts have been made to develop the
machine learning based warfarin dosing algorithms incorporating clinical
factors and genetic variants such as polymorphisms in CYP2C9 and VKORC1. The
most widely validated pharmacogenetic algorithm is the IWPC algorithm based on
multivariate linear regression (MLR). However, with only a single algorithm,
the prediction performance may reach an upper limit even with optimal
parameters. Here, we present novel algorithms using stacked generalization
frameworks to estimate the warfarin dose, within which different types of
machine learning algorithms function together through a meta-machine learning
model to maximize the prediction accuracy. Compared to the IWPC-derived MLR
algorithm, Stack 1 and 2 based on stacked generalization frameworks performed
significantly better overall. Subgroup analysis revealed that the mean of the
percentage of patients whose predicted dose of warfarin within 20% of the
actual stable therapeutic dose (mean percentage within 20%) for Stack 1 was
improved by 12.7% (from 42.47% to 47.86%) in Asians and by 13.5% (from 22.08%
to 25.05%) in the low-dose group compared to that for MLR, respectively. These
data suggest that our algorithms would especially benefit patients required low
warfarin maintenance dose, as subtle changes in warfarin dose could lead to
adverse clinical events (thrombosis or bleeding) in patients with low dose. Our
study offers novel pharmacogenetic algorithms for clinical trials and practice.
