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Optimizing Warfarin Dosing Using Contextual Bandit: An Offline Policy Learning and Evaluation Method

arXiv 2402.11123 ↗First posted 16 Feb 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 16 Feb 2024.

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References · 8

  1. 2021Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimismcited by 275
  2. 2019Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learningcited by 121
  3. 2018Offline A/B testing for Recommender Systemscited by 94
  4. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
  5. 2018Evaluating Reinforcement Learning Algorithms in Observational Health Settingscited by 60
  6. 2018Adapting multi-armed bandits policies to contextual bandits scenarioscited by 8
  7. 2019Online Learning to Estimate Warfarin Dose with Contextual Linear Banditscited by 2
  8. 2021Estimation of Warfarin Dosage with Reinforcement Learningcited by 1

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  1. 2024Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm
  2. 2024OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment
  3. 2022Semi-Markov Offline Reinforcement Learning for Healthcare
  4. 2026Confounding Masquerading as Improvement: A Systematic Evaluation of Offline Reinforcement Learning for Stroke Antithrombotic Treatment in a 129,000-Patient Registry
  5. 2023When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective
  6. 2024CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
  7. 2023RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health Interventions
  8. 2020DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret