Estimation of Warfarin Dosage with Reinforcement Learning
This work addresses dosage estimation for patients on Warfarin, but it appears incremental as it builds on existing bandit methods with modifications.
The paper tackled the problem of estimating proper Warfarin dosage for patients using reinforcement learning, specifically a LinUCB bandit with online supervised learning and reward reshaping, which improved performance over baselines in terms of regret and percent incorrect.
In this paper, it has attempted to use Reinforcement learning to model the proper dosage of Warfarin for patients.The paper first examines two baselines: a fixed model of 35 mg/week dosages and a linear model that relies on patient data. We implemented a LinUCB bandit that improved performance measured on regret and percent incorrect. On top of the LinUCB bandit, we experimented with online supervised learning and reward reshaping to boost performance. Our results clearly beat the baselines and show the promise of using multi-armed bandits and artificial intelligence to aid physicians in deciding proper dosages.