Censoring-Aware Tree-Based Reinforcement Learning for Estimating Dynamic Treatment Regimes with Censored Outcomes
On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery
Abstract (arXiv)
Dynamic Treatment Regimes (DTRs) provide a systematic approach for making sequential treatment decisions that adapt to individual patient characteristics, particularly in clinical contexts where survival outcomes are of interest. Censoring-Aware Tree-Based Reinforcement Learning (CA-TRL) is a novel framework to address the complexities associated with censored data when estimating optimal DTRs. We explore ways to learn effective DTRs, from observational data. By enhancing traditional tree-based reinforcement learning methods with augmented inverse probability weighting (AIPW) and censoring-aware modifications, CA-TRL delivers robust and interpretable treatment strategies. We demonstrate its effectiveness through extensive simulations and real-world applications using the SANAD epilepsy dataset, where it outperformed the recently proposed ASCL method in key metrics such as restricted mean survival time (RMST) and decision-making accuracy. This work represents a step forward in advancing personalized and data-driven treatment strategies across diverse healthcare settings.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 1 of its 35 references that are in the map. First posted on arXiv 9 Mar 2025.
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1 of 35 references.
References · 1
- 2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 40
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- 2024DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime
- 2020DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret
- 2024Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
- 2025POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
- 2026A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning
- 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
- 2026Interaction-Limited Safe Continuous-Time RL for Dynamical Medical Treatment
- 2026Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications