Optimizing Warfarin Dosing Using Contextual Bandit: An Offline Policy Learning and Evaluation Method
This addresses the challenge of safe and effective dosage personalization for patients on warfarin, an incremental improvement in healthcare decision-making using existing methods on new data.
The paper tackled the problem of determining optimal personalized warfarin dosing by using offline policy learning and evaluation with contextual bandits on observational data, resulting in learned policies that outperformed baseline approaches without requiring genotype inputs.
Warfarin, an anticoagulant medication, is formulated to prevent and address conditions associated with abnormal blood clotting, making it one of the most prescribed drugs globally. However, determining the suitable dosage remains challenging due to individual response variations, and prescribing an incorrect dosage may lead to severe consequences. Contextual bandit and reinforcement learning have shown promise in addressing this issue. Given the wide availability of observational data and safety concerns of decision-making in healthcare, we focused on using exclusively observational data from historical policies as demonstrations to derive new policies; we utilized offline policy learning and evaluation in a contextual bandit setting to establish the optimal personalized dosage strategy. Our learned policies surpassed these baseline approaches without genotype inputs, even when given a suboptimal demonstration, showcasing promising application potential.