DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime
CoRR(2024)
Abstract
Reinforcement learning (RL) has garnered increasing recognition for its
potential to optimise dynamic treatment regimes (DTRs) in personalised
medicine, particularly for drug dosage prescriptions and medication
recommendations. However, a significant challenge persists: the absence of a
unified framework for simulating diverse healthcare scenarios and a
comprehensive analysis to benchmark the effectiveness of RL algorithms within
these contexts. To address this gap, we introduce DTR-Bench, a
benchmarking platform comprising four distinct simulation environments tailored
to common DTR applications, including cancer chemotherapy, radiotherapy,
glucose management in diabetes, and sepsis treatment. We evaluate various
state-of-the-art RL algorithms across these settings, particularly highlighting
their performance amidst real-world challenges such as
pharmacokinetic/pharmacodynamic (PK/PD) variability, noise, and missing data.
Our experiments reveal varying degrees of performance degradation among RL
algorithms in the presence of noise and patient variability, with some
algorithms failing to converge. Additionally, we observe that using temporal
observation representations does not consistently lead to improved performance
in DTR settings. Our findings underscore the necessity of developing robust,
adaptive RL algorithms capable of effectively managing these complexities to
enhance patient-specific healthcare. We have open-sourced our benchmark and
code at https://github.com/GilesLuo/DTR-Bench.
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