Instructions to use apoorvasrinivasan/tacrolimus-rl-ddi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use apoorvasrinivasan/tacrolimus-rl-ddi with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="apoorvasrinivasan/tacrolimus-rl-ddi", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
Tacrolimus RL Dosing Agent (LSTM-PPO)
LSTM-augmented PPO agent for adaptive tacrolimus dose adjustment under CYP3A4-mediated drug-drug interactions (DDIs).
Trained on a two-compartment PBPK simulator spanning 8 CYP3A4 inhibitors (fluconazole, voriconazole, posaconazole, ketoconazole, verapamil, diltiazem, erythromycin, clarithromycin) with full patient variability (CYP3A5 genotype, hematocrit recovery, steroid taper, non-adherence, food effects).
Observation space: 8-dim POMDP (trough conc, DDI flag, episode day, dose history) Action space: 9 discrete dose levels (0–5 mg BID) Episode length: 90 days Training: 2M timesteps, 8 parallel envs
Paper: Reinforcement Learning for Adaptive Tacrolimus Dosing with Multi-Drug Interaction Management (ICML 2026)
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