Estimation of the Warfarin Dose

Warfarin has a narrow therapeutic range, yet the appropriate dose can vary substantially with a patient’s demographics, medications, clinical history, and genetics. This project studies whether an online learner can use that context to choose among three weekly dose classes: low (below 21 mg), medium (21-49 mg), and high (above 49 mg).

Implemented Contextual bandits for learning online the dosages of warfarin, including LinUCB and LinTS. The project grew into a broader investigation of data preparation, exploration, and model structure. It is an experimental study, not a clinical prescribing tool.

I used 5,528 patient records from the International Warfarin Pharmacogenetics Consortium dataset. The preprocessing work handles mixed clinical, medication, demographic, and genetic variables; preserves informative missingness; compares K-nearest-neighbor and iterative Bayesian-ridge imputation; and excludes identifiers and post-treatment outcomes that could leak the target. I then compared fixed and published IWPC dosing references with LinUCB, ridge and hybrid ridge variants, a sparse Lasso-based UCB policy, and Linear Thompson Sampling.

The strongest learned configuration so far, a hybrid ridge policy with shared ordinal dose structure, reaches approximately 68.36% mean online accuracy over repeated patient permutations. This improves on the 61.18% fixed-dose baseline and the roughly 64% clinical formula, while remaining slightly below the pharmacogenetic reference near 69%. The GitHub repository and its README contain the full preprocessing rationale, model implementations, experiment notebooks, diagnostics, and generated reports.