  Warfarin is a widely used anticoagulant, and has a narrow therapeutic range.
Dosing of warfarin should be individualized, since slight overdosing or
underdosing can have catastrophic or even fatal consequences. Despite much
research on warfarin dosing, current dosing protocols do not live up to
expectations, especially for patients sensitive to warfarin. We propose a deep
reinforcement learning-based dosing model for warfarin. To overcome the issue
of relatively small sample sizes in dosing trials, we use a Pharmacokinetic/
Pharmacodynamic (PK/PD) model of warfarin to simulate dose-responses of virtual
patients. Applying the proposed algorithm on virtual test patients shows that
this model outperforms a set of clinically accepted dosing protocols by a wide
margin. We tested the robustness of our dosing protocol on a second PK/PD model
and showed that its performance is comparable to the set of baseline protocols.
